A system for extracting glacier boundaries using multiparametric analysis

Abstract

A system for extracting glacier boundaries using multiparametric analysis, consisting of: a data acquisition module configured to acquire Synthetic Aperture Radar (SAR) data of a glacial region; a data processing unit configured to process the SAR data to generate coherence images and backscatter intensity maps, to extract terrain parameters including slope and curvature information from a digital elevation model (DEM), to apply thresholds to the coherence images, backscatter intensity maps, and terrain parameters, to generate a multi-band stack comprising the thresholded coherence images, the thresholded backscatter intensity maps, the thresholded slope information, and the curvature information, and a principal component analysis module configured to standardize the multi-band stack, calculate a covariance matrix from the standardized data, calculate eigenvalues and eigenvectors of the covariance matrix, and transform the original data into a principal component space with reduced dimensionality; a texture feature extraction module configured to calculate first- and second-order statistics from the output of the principal component analysis; generating a texture feature set comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module configured to: convert the set of textural features into a binary format that distinguishes glacier ice from the background; group spatially connected pixels of similar intensity into segments; extract a vector shape file corresponding to the glacier boundary; an output generation module configured to generate a glacier boundary delineation output; and a user interface having a display configured to display the output generated by the output generation module.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to a system for determining glacier boundaries using multiparametric analysis. More specifically, the present invention relates to a system that utilizes synthetic aperture radar (SAR) data to delineate the glacier boundary. The system is configured to perform SAR data processing along with principal component analysis (PCA) and connected component segmentation (CCS) to estimate the glacier boundary. BACKGROUND OF THE INVENTION

[0002] Glaciers are sensitive indicators of climate change and store approximately 69% of the world's freshwater. Monitoring glacier margins is essential for understanding climate impacts, predicting water resources, and assessing geomorphic hazards. Conventional methods for delineating glacier margins are primarily based on optical satellite imagery (such as that used in the Global Land Ice Measurements from Space project). However, these methods have significant limitations, including weather dependence, poor visibility in cloudy conditions, and the difficulty of detecting debris-covered ice.

[0003] Manually delineating glacier boundaries is labor-intensive, time-consuming, and error-prone. While automated methods exist, they still suffer from inaccuracies. Synthetic aperture radar (SAR) offers a promising alternative, operating independently of weather conditions and sunlight and enabling continuous monitoring. Various state-of-the-art approaches utilize SAR coherence and slope information for glacier boundary detection, but have limitations in terms of accuracy and reliability, particularly in complex glacial areas.

[0004] Given the foregoing discussion, it is clear that there is a need for a new approach to determining glacier boundaries. This invention addresses this problem by proposing a multiparametric system that combines SAR data processing with principal component analysis and connected component segmentation to create a semi-automated processing chain for more accurate glacier boundary determination. Summary of the invention

[0005] The present disclosure relates to a system for extracting glacier boundaries using multiparametric analysis. This invention presents a system for determining glacier boundaries using a multiparametric approach that combines synthetic aperture radar (SAR) data processing with principal component analysis (PCA) and connected component segmentation (CCS). The system processes SAR data to generate coherence images and backscatter intensity maps, extracts terrain parameters, and applies advanced analysis techniques to produce precise glacier boundary delineations and velocity measurements.

[0006] To provide a system for extracting glacier boundaries using multiparametric analysis. The system comprises: a data acquisition module for acquiring synthetic aperture radar (SAR) data of a glacier region; a data processing unit for processing the SAR data to generate coherence images and backscatter intensity maps; extracting terrain parameters, including slope and curvature information, from a digital elevation model (DEM); applying thresholds to the coherence images, backscatter intensity maps, and terrain parameters; generating a multiband stack comprising the threshold-based coherence images, threshold-based backscatter intensity maps, threshold-based slope information, and curvature information; a principal component analysis module for standardizing the multiband stack; calculating a covariance matrix from the standardized data; calculating eigenvalues and eigenvectors of the covariance matrix;Transforming the original data into a principal component space with reduced dimensionality; a texture feature extraction module for calculating first- and second-order statistics from the results of the principal component analysis; generating a set of structural features comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module configured to: convert the set of structural features into a binary format that distinguishes glacier ice from the background; group spatially connected pixels of similar intensity into segments; extract a vector shapefile corresponding to the glacier boundary;an output generation module configured to generate an output for delineating the glacier boundary; and a user interface comprising a display configured to display the output generated by the output generation module.

[0007] An objective of the present disclosure is to provide a system for extracting glacier boundaries using multiparametric analysis.

[0008] Another object of the present disclosure is to provide a semi-automatic processing system that can accurately detect glacier boundaries from weather-independent SAR data.

[0009] Another objective of the present disclosure is to improve the accuracy of glacier boundary delineation by integrating multiple parameters, including coherence, backscatter intensity, slope, and curvature information.

[0010] Another object of the present disclosure is to provide a system for measuring glacier velocity using differential SAR interferometry within the same processing framework.

[0011] Another objective of the present disclosure is to overcome the limitations of optical imaging-based glacier monitoring by offering a weather-resistant, high-precision alternative.

[0012] To further clarify the advantages and features of the present disclosure, the invention will be explained in more detail with reference to specific embodiments illustrated in the accompanying drawings. These drawings illustrate only typical embodiments of the invention and are therefore not to be considered as limiting its scope. The invention will be described and explained in more detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0013] These and other features, aspects, and advantages of the present disclosure will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of a system for extracting glacier boundaries using multiparametric analysis according to an embodiment of the present disclosure; Fig. 2 shows a block diagram illustrating the SNAP processing operation performed by the proposed system according to an embodiment of the present disclosure. Fig. 3 shows a block diagram illustrating the GIS processing operations performed by the proposed system according to an embodiment of the present disclosure; and Fig. 4 shows a block diagram illustrating the speed estimation process performed by the proposed system according to an embodiment of the present disclosure.

[0014] Those skilled in the art will also appreciate that the elements in the drawings are shown for convenience and are not necessarily to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may be represented in the drawings by conventional symbols. The drawings may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art from the present description. DETAILED DESCRIPTION:

[0015] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and a clear description thereof. However, the scope of the invention is not limited thereby. Changes and further modifications to the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to one skilled in the art to which the invention pertains.

[0016] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0017] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the language "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not refer to the same embodiment.

[0018] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also additional steps not expressly listed or inherent in that process or method. Likewise, the statement "comprises" for one or more devices, subsystems, elements, structures, or components does not exclude, without further limitation, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. The systems, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0020] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] The functional units described in this specification are referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic systems, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may contain executable code and may consist, for example, of one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct.However, the executable file of an identified device does not have to be physically stored in the same location, but may consist of different instructions stored in different locations which, logically linked, form the device and fulfill its purpose.

[0022] The executable code of a device or module may consist of one or more instructions and may even be distributed across multiple code segments, different applications, and multiple storage devices. Similarly, operational data may be identified and represented within the device and presented in any form and data structure. The operational data may be captured as a single data set or distributed across different storage devices and may be present, at least in part, as electronic signals in a system or network.

[0023] References in this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the phrases "a selected embodiment," "in an embodiment," or "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0024] Furthermore, the described features, structures, or characteristics may be combined in any manner in one or more embodiments. The following description contains numerous specific details in order to provide a thorough understanding of embodiments of the disclosed subject matter. However, those skilled in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details, or with different methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail in order not to obscure aspects of the disclosed subject matter.

[0025] According to the exemplary embodiments, the disclosed computer programs or modules may be executed in a variety of ways, for example, as an application in the memory of a device or as a hosted application on a server that communicates with the device application or browser using various standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in exemplary programming languages that execute from the memory of the device or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0026] Some of the disclosed embodiments involve or otherwise involve the transmission of data over a network, for example, the delivery of various inputs or files over the network. The network may include, for example, the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., PSTN, Integrated Services Digital Network (ISDN), cellular networks, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and / or other transmission or tunneling mechanisms for transmitting data. The network may include multiple networks or subnetworks, each including, for example, a wired or wireless data path. The network may include a circuit-switched voice network, a packet-switched data network, or another network for transmitting electronic communications.For example, the network may include Internet Protocol (IP) or Asynchronous Transfer Mode (ATM) networks supporting voice, such as VoIP, Voice over ATM, or other comparable protocols for voice data communication. In one implementation, the network includes a cellular network configured for the exchange of text or SMS messages.

[0027] Examples of the network include a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), etc.

[0028] Fig. 1 shows a block diagram of a system (100) for extracting glacier boundaries using multiparametric analysis according to an embodiment of the present disclosure.

[0029] Referring to Fig. 1, the system (100) comprises: a data acquisition module (102) configured to acquire synthetic aperture radar (SAR) data of a glacier region; a data processing unit (104) configured to process the SAR data to generate coherence images and backscatter intensity maps; extracting terrain parameters, including slope and curvature information, from a digital elevation model (DEM); applying thresholds to the coherence images, backscatter intensity maps, and terrain parameters; generating a multiband stack comprising the threshold-based coherence images, threshold-based backscatter intensity maps, threshold-based slope information, and curvature information; a principal component analysis module (106) configured to standardize the multiband stack; calculating a covariance matrix from the standardized data; calculating eigenvalues and eigenvectors of the covariance matrix;Transforming the original data into a principal component space with reduced dimensionality; a texture feature extraction module (108) configured to calculate first- and second-order statistics from the output of the principal component analysis; generating a set of structural features comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module (110) configured to convert the set of structural features into a binary format that distinguishes glacier ice from the background; grouping spatially connected pixels of similar intensity into segments; extracting a vector shape file corresponding to the glacier boundary;an output generation module (112) configured to generate an output for delineating the glacier boundary; and a user interface (114) having a display (116) configured to display the output generated by the output generation module (112);

[0030] In one embodiment, the system (100) comprises: a velocity estimation module (118) configured to apply two-stage differential SAR interferometry (DInSAR) to the SAR data; to generate an interferogram from co-registered SAR data pairs; to remove the flat earth phase and the topographic phase from the interferogram; to perform phase deconvolution to obtain unique phase information; and to calculate the line-of-sight (LOS) velocity of the glacier.

[0031] In one embodiment, the data acquisition module (102) is configured to: acquire Sentinel-1 SAR data in Single Look Complex (SLC) format; select specific sub-ranges and bursts corresponding to the area of interest; and update the orbit vectors with ephemeris vectors in the precision state.

[0032] In one embodiment, the data processing unit (104) is further configured to co-register master and slave SAR images with subpixel accuracy, apply intensity adjustment and spectral diversity methods to refine the offsets between the resampled slave and master images, calculate coherence images using an averaging window, and perform multilooking to reduce noise using a rectangular kernel.

[0033] In one embodiment, the data processing unit (104) applies thresholds based on a statistical range defined by the mean plus or minus two times the standard deviation of the respective parameter distributions.

[0034] In one embodiment, the data processing unit (104) is further configured to perform terrain correction using the Range Doppler Terrain Correction technique, reproject the processed data to a particular Universal Transverse Mercator (UTM) zone, and resample all data layers to a common spatial resolution and projection.

[0035] In one embodiment, the principal component analysis module (106) is configured to retain principal components that cumulatively explain at least 90% of the variance in the input data.

[0036] In one embodiment, the texture feature extraction module (108) calculates texture features based on a grayscale co-occurrence matrix (GLCM) using a defined neighborhood window size.

[0037] In one embodiment, the connected component segmentation module (110) implements an eight-connectivity rule for determining spatially connected pixels, wherein the connected component segmentation module (110) is configured to search for neighboring pixels that satisfy a certain condition and group them into the same segment, each segment having pixels of similar intensity that are linked in some way.

[0038] In one embodiment, the velocity estimation module (118) is further configured to filter the interferogram using a nonlinear adaptive algorithm to reduce phase noise; mask the deconvolved phase using the extracted glacier boundary; and convert the deconvolved phase into displacement values based on the radar wavelength.

[0039] In one embodiment, the data acquisition module (102), the processing module (104), the principal component analysis module (106), the texture feature extraction module (108), the connected component segmentation module (110), the output generation module (112), the user interface (114), and the speed estimation module (118) may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like.

[0040] The present invention relates to a system for glacier edge detection and velocity measurement using a multiparametric approach. The data acquisition module is configured to acquire Synthetic Aperture Radar (SAR) data in Single Look Complex format. This module selects specific sub-ranges and bursts according to the respective glacier region and updates the orbit vectors with precise ephemeris vectors to ensure accurate georeferencing.

[0041] The data processing unit serves as the central component and processes raw SAR data using several complex operations. It registers master and slave SAR images with subpixel accuracy, applies intensity adjustment and spectral diversity methods to refine offsets, and calculates coherence images using appropriate averaging windows. Additionally, it extracts terrain parameters, including slope and curvature information, from digital elevation models and applies thresholds based on statistical analysis of parameter distributions. Through terrain correction and reprojection operations, the data processing unit ensures that all data layers share a common spatial resolution and projection. The principal component analysis module reduces the dimensionality of the data while retaining important information.It standardizes the multiband stack containing thresholded coherence images, backscatter intensity maps, slope information, and curvature data. By computing covariance matrices and eigenvalues, as well as eigenvectors, this module transforms the original data into a dimensionally reduced principal component space, preserving the components that explain most of the variance in the input data. The texture feature extraction module improves boundary detection by computing both first- and second-order statistics from the results of principal component analysis. It generates a comprehensive set of textural features, including sum mean, entropy measures, variance parameters, and correlation coefficients. These texture features highlight subtle differences between glacier ice and surrounding terrain that may not be apparent in the original data.The connected component segmentation module implements the novel approach of converting textural feature sets into a binary format to distinguish glacier ice from the background. It groups spatially connected pixels of similar intensity into segments based on connectivity rules, enabling the precise extraction of vector shapefiles corresponding to glacier boundaries. This approach represents a significant advance over previous methods by combining spatial connectivity with intensity similarity. The velocity estimation module provides important information on glacier dynamics by applying two-stage differential SAR interferometry techniques. It generates interferograms from co-registered SAR data pairs, removes flat earth phase and topographic phase components, and performs phase decomposition to obtain unambiguous phase information.The module calculates line-of-sight velocity by converting the unwound phase into displacement values based on the radar wavelength, providing insights into glacier movement patterns. The output generation module produces the final glacier boundary delineation results and velocity measurements in formats suitable for further analysis and integration into geographic information systems. This module ensures that the results are properly georeferenced and formatted for scientific analysis and monitoring applications.

[0042] Fig. 2 shows a block diagram illustrating the SNAP processing operation performed by the proposed system according to an embodiment of the present disclosure.

[0043] Fig. Figure 3 shows a block diagram illustrating the GIS processing operations performed by the proposed system according to an embodiment of the present disclosure.

[0044] Fig. 4 shows a block diagram illustrating the speed estimation process performed by the proposed system according to an embodiment of the present disclosure.

[0045] The system includes a data acquisition module, a data processing module, a principal component analysis module, a texture feature extraction module, a connected component segmentation module, an output generation module, and an optional velocity estimation module, all of which work synergistically to facilitate the extraction and analysis of glacier boundaries using SAR and DEM data.

[0046] The data acquisition module is configured to acquire multitemporal SAR images from Sentinel-1 satellites operating in the C-band with a central frequency of 5.405 GHz. Data acquisition is performed in Interferometric Wide Swath (IW) mode, specifically using TOPSAR (Terrain Observation with Progressive Scans SAR). The implementation uses Level-1 Single Look Complex (L1 SLC) products in VV polarization. The target area, temporal acquisition period, polarization, and acquisition mode are specified via the Alaska Satellite Facility's Vertex online interface. The data are subsequently downloaded via Vertex. As a basis for comparative analyses, the vector shapefile representing the ZEMU glacier boundary is retrieved from the GLIMS (Global Land Ice Measurements from Space) database.For topographic corrections and terrain analysis, the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) with a resolution of one arcsecond is used. This DEM was selected for its compatibility with Sentinel-1 C-band SAR data and its proven accuracy. It exhibits geolocation errors of less than 15 meters and relative elevation errors of less than 10 meters at a spatial resolution of 30 meters.

[0047] The data processing module is implemented using the Sentinel Applications Platform (SNAP). Referring to Fig. 2, as shown in Block I of this figure, processing begins by selecting sub-swath IW3 and bursts 6 to 8 from each SLC image corresponding to the area of interest using the TOPSAR split operator. This selection optimizes computational performance while ensuring consistent spatial coverage across all acquisitions. The orbit vectors for each SLC image are then updated using precision orbit files to enable accurate satellite positioning. The orbit vectors of each SLC dataset were updated using the precision ephemeris vectors. Master-slave image pairs are generated, and the slave image is registered to the master image using intensity matching and spectral diversity methods with subpixel accuracy. During the resampling of the slave-to-master image, deramping and demodulation of the former were performed prior to interpolation.The interpolated slave image was then re-ramped and re-modulated. This ensures azimuthal phase continuity, which is particularly important in TOPS interferometry due to the azimuthal spectrum ramp across bursts. The module applies intensity adjustment and spectral diversity to refine the offsets between the resampled slave and master SLC images, thus ensuring phase continuity between successive TOPS burst edges.

[0048] As in Block II of the Fig. As shown in Figure 2, a coherence image is then generated with a 10×2 pixel averaging window in the range and azimuth directions, taking spatial detail preservation and noise reduction into account. The processing module calculates the coherence image using the SNAP coherence estimation operator with an averaging window size of 10 pixels along the range × 2 azimuth pixels.

[0049] The flat-Earth phase is then removed using satellite orbital and metadata information, and the topographic phase is subtracted using the SRTM DEM, producing a differential interferogram that highlights the line-of-sight shift. The flat-Earth phase was extracted by simulating the phase difference that would occur if the Earth's surface were perfectly flat. This involved calculating the phase difference based on the satellite's geometry, including its baseline, angle of incidence, and distance to each pixel. The simulated flat-Earth phase was then subtracted from the original interferogram to produce a flattened interferogram. The TOPSAR deburst operator is used to eliminate delineation gaps between adjacent bursts that otherwise cause phase discontinuities.Images for all bursts in all substrips of an IW-SLC product were resampled to a common pixel pitch grid in range and azimuth. To reduce noise, multilooking is performed using the SNAP multilook operator. This operator averages the neighboring pixels using a rectangular kernel with four range and one azimuth look. This results in a multilook image with a pixel resolution of 9 m range and 13.9 m azimuth. Goldstein phase filtering reduces phase noise and improves coherence.

[0050] As in Block (III) of Fig. As shown in Figure 2, to account for backscatter intensities, images are generated in the multilooking process by rerunning it. Intensity maps for the master and slave SLCs are generated simultaneously using SNAP's multilooking operator. At this stage of the system, the system has three images consisting of a coherence band and two intensity maps. The Create Stack operator was then used to stack these images on top of each other. These processed coherence and intensity images are then stitched together using the Create Stack operator to ensure spatial alignment and consistency. The resulting stack is corrected for geometric distortions using the Range Doppler Terrain Correction operator, which uses orbit state vectors, radar timing annotations, and the SRTM DEM to convert inclination to ground distance.The corrected stack is reprojected into the UTM zone 45N for geospatial conformity.

[0051] As in Fig. As shown in Figure 3, the data processing module also includes the extraction of terrain parameters using ArcGIS and the Orfeo Toolbox. Coherence and intensity thresholds for glacier regions are defined based on statistical analysis within the range of mean ± 2 × standard deviation. Slope and curvature maps are calculated from the SRTM DEM, resampled, and reprojected according to the SNAP stack resolution. A slope threshold of 53.4°, corresponding to 90% of the slope distribution in the glacier region, is applied to filter out non-glacial terrain.

[0052] Fig. Figure 3 shows that the principal component analysis (PCA) module receives five input bands: thresholded coherence, thresholded master and slave intensities, thresholded slope, and thresholded curvature. These bands are stacked and input to PCA in ArcGIS. PCA standardizes the data, calculates the covariance matrix, and derives the principal components through eigendecomposition. This dimensionality reduction preserves most of the original data variance while facilitating pattern recognition and noise reduction. PCA also improves the representation of anisotropic edges and glacier contours, enabling more precise delineation.

[0053] In Fig. 3, the texture feature extraction module calculates spatial statistics using the grayscale coincidence matrix (GLCM). The module derives thirteen features, including sum mean, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient. These features are calculated using the grayscale coincidence matrix (GLCM) on the quantized input image. This matrix calculates the number of times two adjacent grayscale pixels occur in an image.

[0054] Fig. Figure 3 shows how the Connected Component Segmentation (CCS) module receives the PCA output and performs binary segmentation using the Connected Component Segmentation (CCS) technique available in the Orfeo Toolbox. The input image is binarized to distinguish glacial ice from the background. Spatially connected pixels are grouped based on defined similarity criteria. Each connected region is identified as a segment, and the glacier's vector boundary is extracted from these segments.

[0055] The output generation module consolidates the final glacier boundary in the form of a vector shapefile for the respective area, specifically the Zemu Glacier. This output is suitable for direct integration into GIS platforms and further glaciological analyses.

[0056] Fig.As shown in Figure 4, the velocity estimation module employs a two-stage differential interferometric SAR (DInSAR) technique. The module uses co-registered SLC pairs to generate an interferogram. After removing the flat-Earth and topographic phases, the residual phase due to the displacement is unpacked using the SNAPHU algorithm, which applies statistical cost network flow optimization. This unpacked phase facilitates the calculation of the line-of-sight displacement, which is subsequently converted into velocity values, thus enabling the assessment of glacier dynamics.

[0057] The proposed system thus enables the automatic extraction of glacier boundaries through multiparametric SAR and DEM analysis, complemented by an optional velocity estimation for dynamic monitoring.

[0058] The system described here is configured for semi-automated processing for glacier margin detection and utilizes synthetic aperture radar (SAR) data, principal component analysis (PCA), and connected component segmentation (CCS). The primary goal is the precise and reproducible delineation of glacier margins through an integrated analysis pipeline that combines radiometric, geometric, and spatial features from remote sensing data. The SAR data processing component of the system provides weather-independent, high-resolution images that capture surface features important for glacier delineation, such as radar backscatter intensity and coherence. These parameters are critical for distinguishing glacier ice from the surrounding terrain, especially in regions of persistent cloud cover or optical occlusion.The system's integrated PCA component reduces data dimensionality by identifying principal components that capture the greatest variance within the input data stack. This highlights relevant spatial and structural features, such as edges and patterns of glacier structures, while suppressing noise and redundant information. Dimensionality reduction also improves interpretability and computational performance in subsequent processing steps. The system's CCS component performs binary segmentation by grouping pixels of similar intensity into spatially contiguous segments. This segmentation strategy, based on pixel connectivity and intensity similarity, enables precise extraction of glacier boundaries while preserving spatial relationships in glaciated terrain.

[0059] The uniqueness of the system lies in the specific combination of parameters selected for glacier identification. In contrast to conventional approaches based solely on coherence and slope, the new methodology integrates radar backscatter intensity and terrain curvature in addition to coherence and slope parameters. This multiparametric approach provides a more robust input dataset and increases the reliability of the extracted glacier outlines, particularly in regions with complex surface morphology. The use of CCS for segmentation introduces additional features, as this offers advantages in delineating contiguous glacier areas, even in the presence of debris cover. The comparative evaluation of the delineated glacier boundary with the GLIMS database shows qualitative agreement with a calculated Intersection over Union (IoU) value of 0.67.This value underscores the effectiveness of the proposed system in challenging environments such as the Zemu Glacier, which is characterized by significant debris cover that reduces SAR coherence. Furthermore, the system includes a velocity estimation module based on two-pass differential SAR interferometry (DInSAR). This enables the estimation of line-of-sight (LOS) velocity, which provides insights into the glacier's dynamic movement over time. Such velocity measurements are critical for long-term monitoring of glacier movement and contribute to the assessment of glacier health in the context of climate change.

[0060] The integrated system, demonstrated using Sentinel-1 SAR data over the Zemu Glacier in the Eastern Himalayas, provides a reproducible and scalable framework for glaciological analyses. The system's architecture allows adaptation to other glacial regions, thus facilitating comparative studies in diverse geographical contexts. The approach contributes significantly to the development of remote sensing applications in glaciology by overcoming the limitations of optical imaging and manual delineation methods. By synergistically leveraging the strengths of SAR data processing, PCA, and CCS, the system enables comprehensive glacier monitoring, including boundary delineation and motion analysis. The achieved IoU of 67% is remarkable given the dense debris cover and complex surface properties of the Zemu Glacier, which traditionally hamper accurate delineation using conventional methods.The importance of precise glacier boundary mapping goes beyond academic interest. It plays a pivotal role in researching glacier dynamics, assessing the impacts of climate change, water resource management, and evaluating geomorphic hazards. The proposed system supports these goals by providing reliable and repeatable results that can inform regional water management strategies, particularly for glacier-fed river systems such as the Teesta. It also supports the identification and mitigation of hazards such as glacial lake outbursts (GLOFs). To improve performance in the future, researchers can integrate data from multiple sources, including optical or thermal imagery combined with SAR data, particularly in debris-laden areas. Temporal analyses using multi-epoch datasets can further refine change detection and trend analysis.The inclusion of high-resolution reference data from UAV platforms or ground surveys can further improve accuracy. The integration of machine learning or deep learning techniques could increase segmentation precision, especially in challenging glacial terrain. Furthermore, seasonal observations can help reduce temporal biases and improve the generalizability of the methodology to varying climate and surface conditions. These improvements would contribute to more robust modeling of glacier behavior and the prediction of associated risks. The proposed system addresses critical gaps in current glacier monitoring practices. It provides a scalable, accurate, and semi-automated solution for detecting glacier boundaries and estimating glacier movement.The results underscore the importance of regular monitoring of Himalayan glaciers using SAR data and support data-driven strategies in climate adaptation planning, habitat conservation, and regional water management. The methodological framework can be adopted globally, promotes consistency in glacier monitoring, and supports joint climate resilience initiatives.

[0061] The drawings and the foregoing description illustrate examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the order shown; nor do all actions need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0062] Advantages, further benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, advantages, solutions to problems, and any components that may result in or enhance an advantage, advantage, or solution are not to be construed as critical, required, or essential features or components of any or all of the claims. REFERENCES 100 A system for extracting glacier boundaries using multiparametric analysis. 102 Data acquisition module 104 Data processing unit 106 Principal Component Analysis Module 108 Texture feature extraction module 110 Segmentation module for connected components 112 Output generation module 114 User interface 116 ad 118 Speed estimation module 202 S1a Slc slave 202a Partial swath distribution 202b Precise Orbit Ephemerides 202c Co-registration (reverse geocoding + Esd) 202d Srtm Dem 202e Precise Orbit Ephemerides 202f S1a Slc Master 204 Coherence Estimation (Flat Earth and Topographic Phase Distance) 204a Topsar - Unloading and merging 204b Multilooked for averaging inherent noise 206 Topsar - Unloading and Merging 206a Multilooked for averaging inherent noise 206b Backscatter intensities 208 Collocation by Stacking 210 Speckle Filter with Lee-Sigma Approach 212 Terrain Correction (Projected to UTM Zone 45n) 214 stacks of coherence and two backscatter intensity images 216 GIS processing 218 Orfeo Toolbox Processing 302 Inclination and Curvature of Srtm Dem 304 Reprojecting and resampling into stacked coherence and intensity images 306 thresholds for coherence, intensities and gradient 308 stacks of coherence and two backscatter intensity images 310 Principal Component Analysis 312 Haralick texture extraction 314 Connected Segmentation 316 Vector Shapefile of the Glacier 318 Curvature From The 402 Data pair co-registration 404 Interferogram generation 406 Topsar - Unloading 408 Nonlinear Adaptive Algorithm Phase Filtering 410 Multilook 412 Phase Unpacking Using the Minimum Cost Flow Method 414 Line of sight shift and corresponding speed 416 Terrain-corrected starting speed

Claims

[1] A system for extracting glacier boundaries using multiparametric analysis, consisting of: a data acquisition module configured to acquire Synthetic Aperture Radar (SAR) data of a glacial region; a data processing unit configured to process the SAR data to generate coherence images and backscatter intensity maps, to extract terrain parameters including slope and curvature information from a digital elevation model (DEM), to apply thresholds to the coherence images, backscatter intensity maps, and terrain parameters, to generate a multi-band stack including the thresholded coherence images, the thresholded backscatter intensity maps, the thresholded slope information, and the curvature information, and a principal component analysis module configured to standardize the multi-band stack, calculate a covariance matrix from the standardized data, calculate eigenvalues and eigenvectors of the covariance matrix, and transform the original data into a principal component space with reduced dimensionality; a texture feature extraction module configured to calculate first- and second-order statistics from the output of the principal component analysis; generating a texture feature set comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module configured to: convert the set of textural features into a binary format that distinguishes glacier ice from the background; group spatially connected pixels of similar intensity into segments; extract a vector shape file corresponding to the glacier boundary; an output generation module configured to generate a glacier boundary delineation output; and a user interface having a display configured to display the output generated by the output generation module. [2] The system of claim 1, further comprising: a velocity estimation module configured to apply two-stage differential SAR (DInSAR) interferometry to the SAR data; to generate an interferogram from co-registered SAR data pairs; to remove the flat earth phase and the topographic phase from the interferogram; to perform phase deconvolution to obtain unique phase information and to calculate the line-of-sight (LOS) velocity of the glacier. [3] The system of claim 1, wherein the data acquisition module is configured to acquire Sentinel-1 SAR data in Single Look Complex (SLC) format, select specific sub-areas and bursts corresponding to the area of interest, and update orbit vectors with ephemeris vectors in the precision state. [4] The system of claim 1, wherein the data processing unit is further configured to co-register master and slave SAR images with sub-pixel accuracy, apply intensity adjustment and spectral diversity methods to refine the offsets between the resampled slave and master images, compute coherence images using an averaging window, and perform multilooking to reduce noise using a rectangular kernel. [5] The system of claim 1, wherein the data processing unit applies thresholds based on a statistical range defined by the mean plus or minus twice the standard deviation of the respective parameter distributions. [6] The system of claim 1, wherein the data processing unit is further configured to perform terrain correction using the Range Doppler Terrain Correction technique, reproject the processed data to a particular Universal Transverse Mercator (UTM) zone, and resample all data layers to a common spatial resolution and projection. [7] The system of claim 1, wherein the principal component analysis module is configured to retain principal components that cumulatively explain at least 90% of the variance in the input data. [8] The system of claim 1, wherein the texture feature extraction module calculates texture features based on a gray level co-occurrence matrix (GLCM) using a defined neighborhood window size. [9] The system of claim 1, wherein the connected component segmentation module implements an eight-connectivity rule for determining spatially connected pixels, wherein the connected component segmentation module is configured to search for neighboring pixels that satisfy a certain condition and group them into the same segment, each segment having pixels of similar intensity that are linked in some way. [10] The system of claim 2, wherein the velocity estimation module is further configured to filter the interferogram using a nonlinear adaptive algorithm to reduce phase noise; mask the deconvolved phase using the extracted glacier boundary; and convert the deconvolved phase into displacement values based on the radar wavelength.

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