51 results about "Remote sensing application" patented technology

The invention belongs to the technical field of remote sensing super-resolution images, and particularly relates to a remote sensing image super-resolution reconstruction method based on an adaptive gating Transform. The method comprises the following steps: S1, constructing a training set; s2, constructing a self-adaptive gated Transform super-resolution reconstruction network, and training the self-adaptive gated Transform super-resolution reconstruction network by using the training set, so as to obtain a self-adaptive gated Transform super-resolution reconstruction model; and S3, inputting a remote sensing low-resolution image to be reconstructed into the adaptive gating Transform super-resolution reconstruction model to obtain a super-resolution reconstruction image. The invention aims to break through the limitation of an existing method in the aspects of efficiency, detail recovery and multi-scale fusion, provides a super-resolution solution with high fidelity and high efficiency for remote sensing images, and meets the urgent demand of refined remote sensing application for high-quality images.

The invention discloses a multi-temporal optical remote sensing image robust registration method based on multi-scale joint similarity measurement, and the method comprises the steps: 1, inputting a dual-temporal image, and obtaining uniformly distributed control point pairs through SIFT extraction and interactive manual auditing; 2, constructing a global dense displacement field by using a thin-plate spline, completing coarse registration and partitioning according to an overlapping strategy; 3, performing three-stage coarse-to-fine template matching on each image block, inheriting an initial displacement value step by step, and realizing radiation difference and geometric deformation synchronous self-adaption by taking a product of normalized mutual information and a normalized cross correlation coefficient as joint similarity measurement; and 4, performing sub-pixel-level deformation correction on the reliable displacement field by using a local thin plate spline, and outputting a registration image and metadata. According to the method, a global sparse-local dense framework and a multi-scale progressive + joint measurement + quality constraint mechanism are coupled, and a high-robustness and high-precision registration solution is provided for quantitative remote sensing application such as change detection and disaster assessment.

The invention discloses a prompt tuning method during testing based on uncertainty perception and cache driving, and belongs to the technical field of computer vision. The method comprises the following steps: firstly, extracting visual features from an input remote sensing image, and retrieving a most relevant prompt vector as initialization in a prompt cache according to feature similarity; then performing model reasoning by utilizing initialization prompt, judging the uncertainty of the sample based on an entropy value of a prediction result, executing prompt optimization on a high-confidence sample, optimizing prompt parameters through entropy minimization, and skipping updating on a low-confidence sample; reasoning again to obtain a final classification result after prompt tuning is completed; meanwhile, the prompt cache is updated according to the sample confidence, and the representativeness and timeliness of prompts in the cache are kept. The method improves the adaptability and prediction stability of the model to the distribution change remote sensing image, and is suitable for the remote sensing application scene with lack of annotation and uncertain distribution.

This application relates to the fields of SAR signal processing and marine remote sensing applications, and provides a method and apparatus for sea surface current field inversion based on electromagnetic vortex wave radar data. The method determines the Doppler spectrum using electromagnetic vortex wave radar image data, obtains the measured frequency position of the trough feature point in the Doppler spectrum, and determines the theoretical frequency position using the position and angle of the two main lobes and the angle of the trough feature point in the radar antenna pattern. Then, the difference between the measured frequency position and the theoretical frequency position is used to obtain the anomalous frequency offset. Next, the Doppler frequency offset caused by wind and wave motion is removed from this anomalous frequency offset to obtain the target Doppler frequency offset. Finally, the radial velocity of the sea surface is calculated using the Doppler frequency offset, thus realizing sea surface current field inversion. This effectively expands the applicability of the Doppler frequency shift-based ocean current inversion method and improves the application potential of electromagnetic vortex wave radar in sea surface current field detection.

The invention discloses a boundary perception gating cross-scale feature fusion method for a remote sensing image, belongs to the technical field of computer vision and remote sensing image processing, and particularly relates to the field of remote sensing target detection. Aiming at the problems of insufficient cross-layer alignment, boundary confusion, insufficient small target recall and the like in the prior art, the invention provides a boundary perception gating cross-scale feature fusion method for a remote sensing image. The method comprises the following steps: constructing an Epag module containing Scharr edge prior and logit space fusion, and realizing dual-branch feature adaptive fusion; designing an SAFM module with channel separation and multi-scale pooling, and completing spatial adaptive enhancement; the two modules are integrated at the neck of a detection network, and multi-scale features are output through top-down gating fusion and spatial modulation. According to the method, the target boundary positioning precision and the multi-scale adaptation capability are remarkably improved, multiple technical pain points are solved, mAPs on DOTA1.0 and DIOR-R data sets are improved respectively, multiple detectors are adapted, and accurate technical support is provided for remote sensing application such as urban planning and traffic monitoring.

The invention provides a multi-modal large model quantitative fine tuning method and device for remote sensing application. The method comprises the following steps: quantizing a backbone network in a trained multi-modal large model to obtain a quantized backbone network; training a side chain network in the second network model based on a data set for remote sensing semantic segmentation and remote sensing target detection to obtain a trained second network model; testing performance indexes of the trained second network model based on a test set; when the performance index of the trained second network model meets the performance index of the unmanned aerial vehicle, stopping training the side chain network in the trained second network model to obtain a trained second network model; the second network model comprises a quantized backbone network and a quantized side chain network; the side chain network is a lightweight network; the trained second network model is used for being deployed on the unmanned aerial vehicle so as to perform semantic segmentation or target detection on the to-be-processed remote sensing image.

The application discloses a canopy coverage considering aggregation index inversion method and correction method, relates to the technical field of remote sensing image processing and aggregation index inversion, collects remote sensing images of a vegetation canopy, extracts hot spot reflectivity and dark spot reflectivity respectively, calculates a normalized hot spot and dark spot index NDHD, calculates an initial value of an aggregation index CI according to a linear relationship between the NDHD and the CI, and corrects the initial value of the CI by using a canopy coverage Ccan, wherein an error between the initial value of the CI and a real value of the CI has a linear relationship with the Ccan. The application first establishes a CI correction model based on the Ccan, and solves the deviation problem of a traditional algorithm in a sample field with high or low Ccan. By improving a mainstream CI inversion algorithm based on the NDHD, the application significantly improves the precision of CI inversion, and provides more reliable technical support for forest ecological monitoring, carbon cycle research and remote sensing application.

This invention relates to the field of remote sensing image processing technology, specifically disclosing a remote sensing image target detection method based on a multi-scale, cross-stage network model. The method includes the following steps: acquiring the remote sensing image to be tested; inputting the image into the backbone network after passing through an input layer, extracting multi-scale and multi-level semantic features step-by-step, and obtaining feature maps of different sizes through multi-scale downsampling; inputting the feature maps of different sizes into a feature integration network for cross-stage fusion and lightweight optimization to obtain multiple fused feature maps; and inputting the multiple fused feature maps into a prediction head for classification and detection to obtain detection results at multiple scales. This invention, by constructing a novel multi-scale, cross-stage network model, enhances the capabilities of multi-scale feature extraction, fusion, and target recognition, enabling accurate detection of multi-scale targets, especially small and complex-shaped targets, in remote sensing images. It balances detection accuracy and inference efficiency, making it suitable for remote sensing applications such as marine monitoring and urban planning.

This application provides a method, apparatus, equipment, and storage medium for identifying agricultural irrigation districts based on the irrigation-enhanced evaporation stress index (IESI), relating to the fields of agricultural water resources monitoring and management and remote sensing applications. The method includes: constructing a multi-source remote sensing and auxiliary dataset for the target area; calculating potential evapotranspiration and actual evapotranspiration and coupling them with a vegetation growth stability factor to obtain quarterly IESI data for the target area; determining irrigation identification thresholds applicable to plains and mountainous areas respectively; and discriminating the IESI of the target area to generate a binary spatial distribution map of irrigated and non-irrigated areas. This application solves the problems of existing irrigation area identification methods in large-scale applications, such as reliance on statistical data or large training samples, strong empirical thresholds, insufficient adaptability to terrain and regional differences, and complex calculation processes. It achieves irrigation behavior identification centered on evaporation stress response, ensuring computational efficiency while also considering spatial accuracy and regional adaptability.

The invention belongs to the technical field of remote sensing application, and relates to a knowledge migration type high-resolution remote sensing image building change detection method and system. The method comprises the following steps: constructing an adaptive knowledge-driven building change detection model containing multi-layer attention; obtaining a dual-time-phase high-resolution remote sensing image, and constructing a dual-time-phase high-resolution remote sensing image data set; training a building change detection model by using the double-time-phase high-resolution remote sensing image data set; storing solidification model parameters corresponding to each dual-time-phase high-resolution remote sensing image data set into a weight pool; and calling matched curing model parameters from the weight pool according to a data set scene of a to-be-detected remote sensing image, loading the parameters to the MMA-Net network, outputting a binary change graph, and completing building change detection. According to the method, a knowledge accumulation mode of adaptively modeling attention by using a weight pool is provided, model parameters obtained by training different data are stored to form the weight pool, and the weight pool is reused in a new building change detection task to realize migration application.

The application discloses a global average sea clutter near real-time estimation method, system, device and storage medium, including the following steps: obtaining multi-source satellite-borne radar historical backscattering coefficient data, and performing time and space matching and binning on the ERA5 wind field; an average sea clutter estimation model is established according to a geophysical model function fitting; the satellite-borne microwave scatterometer wind field is corrected in deviation and fused with a numerical weather prediction wind field, so that a high-coverage, continuous near real-time global sea surface wind field is obtained; the relative azimuth is calculated in combination with radar observation parameters and fused wind direction, and global average sea clutter data is obtained by using the model; the application improves the precision and application range of sea clutter estimation under the condition of medium and small incident angles, provides a high-quality input wind field through a multi-source wind field fusion technology, realizes rapid, stable and near real-time estimation of global average sea clutter, has high timeliness and business feasibility, and can meet the needs of near real-time sea clutter monitoring and ocean remote sensing application.

The application focuses on the field of agricultural informatization and remote sensing application technology, and particularly relates to a kind of alfalfa yield estimation method based on multispectral and physical constraint sample enhancement. The method first acquires unmanned aerial vehicle multispectral image data corresponding to the alfalfa growth period, and lays out field quadrats with equal area in the image coverage area, collects measured data of alfalfa hay yield, leaf area index, chlorophyll content and leaf equivalent water thickness, and constructs an initial sample set. Then, input each parameter data within a reasonable range, generate predicted multispectral samples that meet the physical consistency constraint using the radiation transfer model, to expand the sample space. The measured samples and predicted samples are fused to construct an alfalfa hay yield estimation training data set. Based on the training data set, a mapping model between unmanned aerial vehicle multispectral features and alfalfa hay yield is established, and the alfalfa hay yield is estimated through machine learning.

This invention introduces a method and system for auto-labeling data through a hybrid Quantum-Classical approach. Initially, data is acquired, converted to a usable format, and reference data points for target objects are extracted and data is smoothened, reduce its dimensionality via Principal Component Analysis. The quantum machine learning (QML) component is then applied to validate the data, leveraging quantum algorithms for enhanced accuracy and efficiency. Grouping of similar data points occur utilizing statistical techniques, with a threshold ensuring only highly similar data points are selected from one target reference data point as input along with target area. The validated data is auto-labeled using QML, significantly enhancing the efficiency and accuracy of data analysis. Embodiments of this method are particularly beneficial for remote sensing applications such as environmental monitoring, agricultural assessment, urban planning and defense uses, providing precise classification of land cover and materials.

This application discloses a method, system, equipment, medium, and product for assessing the susceptibility of single-gully debris flows, relating to the fields of geological disaster prevention and remote sensing application technology. The method includes: acquiring remote sensing images and LiDAR data of a target watershed, and identifying and interpreting gully and slope debris sources within the target watershed; extracting a first feature parameter for each interpreted slope debris source and a second feature parameter for each interpreted gully debris source; calculating the slope debris source contribution based on the first feature parameter and the gully debris source contribution based on the second feature parameter; substituting the slope and gully debris source contributions into a debris flow susceptibility probability calculation model to calculate a debris flow susceptibility probability value; and classifying debris flow gullies in the target watershed into different susceptibility levels based on the debris flow susceptibility probability value to assess the susceptibility of single-gully debris flows. This application enables refined and categorized debris flow susceptibility assessment.

The invention belongs to the technical field of water body ecological risk assessment, relates to remote sensing image processing, environmental factor assessment and risk assessment, and aims to solve the problem that the prior art is short in remote sensing application, super-resolution fusion and risk diagnosis connection. The invention provides a large-scale aquatic vegetation imbalance risk differentiation diagnosis method based on super-resolution remote sensing. According to the method, a large-scale aquatic vegetation spatio-temporal information extraction method is constructed based on a remote sensing super-resolution reconstruction technology, that is, vegetation class groups, biomass and spatial distribution information are accurately extracted through super-resolution high-resolution remote sensing data. Meanwhile, a driving mechanism of multiple stress factors such as hydrological situation change, water quality pollution and habitat disturbance on aquatic vegetation under a large scale is disclosed by combining a multi-source data system. Finally, an evaluation system is constructed according to the evaluation indexes, systematic diagnosis of'classification-grading-zoning 'is achieved, and a global and differentiated treatment scheme is provided for water bodies of the whole key watershed, large lakes or cross-administrative districts.

This invention discloses a zero-shot open-set remote sensing scene classification method based on multimodal prototypes. It primarily overcomes the limitations of traditional methods in complex remote sensing scenes, which suffer from low classification accuracy and poor unknown class rejection capabilities due to alignment differences between visual and linguistic modalities. This invention expands the semantic space from broad and singular to comprehensive and refined by introducing surrogate unknown class labels and fine-grained textual prompts. Simultaneously, it utilizes high-confidence samples to construct visual prototypes reflecting the true visual distribution, and dynamically reconstructs textual prototypes based on these prototypes. During inference, bidirectional alignment between visual and textual data effectively balances accurate classification of known remote sensing scenes with robust rejection of unknown interference items, facilitating efficient and secure model deployment in remote sensing applications with unlabeled data and dynamically changing environments.

This application relates to the field of hyperspectral remote sensing application technology, and provides a hyperspectral methane detection method based on spatial-spectral joint analysis. First, a sample dataset is constructed, and simultaneously, a methane detection neural network is built to extract the spatial-spectral features of the methane plume from the sample dataset. Then, a preprocessing module based on fundamental laws is established to form a hard constraint mechanism, outputting standardized spatial-spectral features of the methane plume to obtain a predicted methane concentration distribution map. A physical information penalty term is constructed to form a soft physical constraint, minimizing the error between the predicted methane concentration distribution map and the pseudo-true label, thus incentivizing the network to generate a full-resolution methane concentration distribution map. Through a deeply fused spatial-spectral joint strategy, relying on the synergistic effect of the hard constraint mechanism and the soft physical constraint, effective suppression of background noise can be achieved in complex atmospheric environments and various background interference scenarios, significantly improving the accuracy and reliability of signal detection. The detection efficiency is high, making it suitable for large-scale and efficient processing of massive hyperspectral data.

The present application relates to remote sensing target detection technical field, especially in kind, a kind of remote sensing target detection method and device based on multilevel knowledge distillation, comprising: the remote sensing image is input respectively teacher model and student model, extracts multilevel feature;With shift local feature collaborative distillation module, low-level feature is distilled, and the identification ability of student model to small target is enhanced using local space feature modeling;With deep layer pixel-by-pixel distillation module, high-level feature is distilled pixel by pixel, and classification ability is improved;The angle prediction of teacher network and student network is generated and is antagonized and distilled, and the precision of angle prediction is improved;Finally, the student model is optimized in combination with each loss.The student model trained by the present application significantly improves the prediction accuracy of remote sensing target, while maintaining high prediction efficiency, adapting to the actual needs of remote sensing application scenarios.

The application discloses a kind of spatiotemporal spectrum joint super-resolution reconstruction methods based on giant remote sensing star group, including from multiple isomerism satellites acquisition same area time series, multi-angle and multispectral data and carry out radiation calibration and atmospheric correction;Subpixel level alignment of multi-source data is realized using joint registration model;Three-dimensional convolution is used to extract time-varying characteristics, two-dimensional convolution is used to extract spatial structure and texture characteristics, and one-dimensional convolution is used to extract and reduce dimension spectral characteristics;Through attention mechanism, time, space and spectral characteristics are adaptively weighted and fused to generate joint feature tensor;Super-resolution reconstruction is carried out, and high spatial resolution, high time resolution and high spectral fidelity target image are obtained.The method considers resolution improvement and spectral authenticity, and is suitable for fine city mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, long-range reconnaissance, target change detection and damage assessment and other high-precision remote sensing application scenarios.

The invention discloses a satellite-ground cooperative intelligent remote sensing calculation method and system based on generalized dynamic guidance, and relates to the technical field of space information technology, artificial intelligence and edge calculation crossing. According to the method, generalized dynamic guidance information including an intelligent space mask, a task adaptive prompt vector and a collaborative reasoning instruction is formed through a ground guidance center; the satellite-borne processing unit realizes self-adaptive focusing processing by adopting local focusing calculation and a conditional premature exit mechanism according to the guide information and in combination with a real-time resource state; and meanwhile, a space-ground closed-loop optimization system is constructed, and a guide strategy is continuously optimized through result feedback. According to the method, the mode conversion from full-image processing to focusing calculation is realized, the on-satellite processing efficiency is remarkably improved, the calculation amount is reduced by 70-90% while the precision is ensured, the satellite-ground communication data volume is reduced by more than 95%, the problems of on-satellite resource limitation and data transmission bandwidth bottleneck are effectively solved, and the method is suitable for large-scale popularization and application. The method has important value in remote sensing application scenes such as disaster emergency and environment monitoring.

The application belongs to the technical field of remote sensing application, and relates to a soil moisture estimation method for irregular asynchronous multi-source remote sensing time series data. The method comprises the following steps: acquiring multi-source remote sensing time series data, generating a binary time mask matrix, and extracting a soil moisture dynamic anomaly sequence; a sequence-to-sequence soil moisture estimation model is constructed to obtain pre-training model parameters; a dynamic mask-based mean square error loss is calculated, the model is fine-tuned, and a soil moisture anomaly field is output; a preset period mean field is reconstructed, and the soil moisture anomaly field is superimposed on the preset period mean field to obtain a spatiotemporally continuous high-resolution absolute soil moisture estimation result. The application solves the problem that high resolution and time continuity cannot be achieved simultaneously, enables the model to support missing data, eliminates the interference of systematic mean deviation on cross-region migration, significantly improves the cross-region migration capability of the model, and significantly improves the spatial reconstruction accuracy and generalization of soil moisture under sparse site conditions.

The application relates to a method and system for measuring rock minerals based on AI artificial intelligence, and relates to the field of hyperspectral remote sensing applications, and solves the problem that the existing division of the distribution area of rock minerals is relatively subjective and is prone to large division deviation, and the method comprises the following steps: obtaining spectral data of rock minerals to be classified and an obtained position; according to a preset corresponding relationship between rock mineral types and spectral data, the spectral data of the rock minerals to be classified and the obtained position, the type of the rock minerals to be classified and the obtained position are analyzed and determined; according to the type of the rock minerals obtained by the history of the obtained position and the type of the rock minerals to be classified analyzed this time, whether the type of the rock minerals to be classified analyzed this time falls into the type of the rock minerals obtained by the history of the obtained position is analyzed and determined. The application has the following effects: through the artificial intelligence measurement mode, the efficiency and accuracy of measuring the distribution area of rock minerals are improved.

The application belongs to the technical field of remote sensing application, and specifically discloses a copper-lead-zinc mineralization alteration information extraction method and system based on multispectral remote sensing. The method receives multispectral remote sensing image data and generates surface reflectivity data; the aforementioned data is processed based on a spectral feature library to identify target mineral combinations and quantitatively delineate spatial distribution of alteration zoning; vegetation coverage areas are eliminated and background interference is suppressed to generate an alteration intensity grading map; a visual map is generated based on the grading map; a comprehensive report is generated and suggestions for anomaly grade evaluation and ore prospecting target area delineation are proposed; multispectral remote sensing image data, surface reflectivity data and the alteration intensity grading map are archived; and an interface is used to realize interaction with a remote sensing platform and GIS software. The system comprises remote sensing data acquisition, spectral preprocessing, alteration mineral identification, information extraction, result output, data storage and management and system integration modules. The application has the characteristics of high efficiency and precision, clear extracted information and strong practicality.

The invention relates to the technical field of grassland ecological monitoring and remote sensing application, in particular to a method and system for monitoring grassland shrubs based on unmanned aerial vehicle remote sensing, and the method comprises the steps: determining a fluctuation coefficient representing the physiological stability of the shrubs based on an unmanned aerial vehicle multispectral image and ground actual measurement data; determining fractal dimensions representing boundaries of shrub communities on the basis of multiple high-resolution orthoimages of the unmanned aerial vehicle; based on the unmanned aerial vehicle multispectral image and ground actual measurement data, determining a soil moisture spatial heterogeneity entropy representing the shrub local hydrological environment reconstruction intensity; solving an invasion and occupation risk index; predicting to obtain a state transition probability; obtaining a system instability factor; judging the relationship between the system instability factor and a preset intervention threshold; if the system instability factor is greater than the preset intervention threshold, generating an intervention instruction; if not, generating a maintenance instruction; according to the method, the practical guiding significance of a prediction result is ensured, and the accuracy and robustness of an evaluation system are integrally improved.

The application belongs to the technical field of remote sensing application, and provides a grassland biomass inversion method based on a radiation transmission model and machine learning, comprising the following steps: obtaining and processing remote sensing data, DEM data and measured biomass data of a target region; performing terrain correction by adopting an SCS+C method; performing global sensitivity analysis on input parameters in a PROSAIL model, and calibrating the input parameters according to the global sensitivity analysis; introducing a terrain factor into the model, and correcting a solar zenith angle and an observation zenith angle; generating simulated canopy reflectance data by using the model, and constructing a data set; constructing a lookup table LUT by combining the simulated canopy reflectance data and aboveground biomass data, and performing machine learning modeling according to a canopy spectral data set; verifying inversion precision, and outputting a grassland aboveground biomass spatial distribution map. The inversion method introduces a terrain factor on the basis of a traditional model, and improves adaptability and estimation precision of the model to a complex terrain region by combining a machine learning method.

The invention discloses a surface water body intelligent extraction method and system based on multi-source remote sensing image data, and belongs to the technical field of multi-source remote sensing. High-spatial-resolution visible light and near-infrared remote sensing images in different time phases are collected, image registration and radiometric calibration are carried out, and a unified time sequence image sequence is constructed. And based on the normalized water body index, extracting water body response change characteristics of each pixel, identifying a water body change salient region and an occlusion mutation existence region, and generating a corresponding binary mask graph. Constructing three-channel image input data and a change guide layer by combining spectral characteristics and change information, guiding and prompting to generate network output points, frames or mask prompt information, inputting the information into an SAM mask decoder to obtain water body mask graphs of all time points, dividing permanent and seasonal water bodies according to stability indexes, generating a complete water body distribution graph, and outputting the water body distribution graph to a water body distribution graph. The method improves the intellectualization and stability of water extraction in a complex earth surface scene, and is suitable for various remote sensing application scenes such as water resource monitoring and water environment evaluation.

The invention belongs to the technical field of remote sensing application, and relates to a soil moisture downscaling method based on deep learning and multi-source data fusion. The method comprises the steps of data acquisition, data space-time registration, abnormal value elimination and standardization preprocessing. Constructing a downscaling feature set; constructing a training set and a test set; a CNN-LSTM mixed downscaling model is constructed; performing iterative training to obtain a trained downscaling model; performing multi-dimensional precision evaluation and model global precision evaluation on the model; and outputting the soil moisture data set with the target resolution. Through the CNN-LSTM hybrid downscaling model fusing the multi-scale convolutional neural network and the bidirectional long short-term memory network, the situation that a single model can only capture single-dimension features is avoided, and the model fitting precision is remarkably improved; through a training strategy, overfitting is effectively inhibited, and the generalization ability and the space robustness of the model are improved; and the reliability and consistency of downscaling products are ensured through a truth-value-free verification method.

The application discloses a zero-sample field plot segmentation method for complex farmland landscape, and relates to the technical field of remote sensing image intelligent interpretation and agricultural informatization. The method comprises the following steps: acquiring a target region RGB remote sensing image and cutting the image into a preset size image set, segmenting the image set through a SAM method to obtain a ground feature mask and a confidence score, constructing a field plot sample set, dividing the sample set to train an instance segmentation model, and finally applying the trained model to target region field plot segmentation. The application realizes zero-sample field plot segmentation, breaks through the limitations of traditional deep learning sample dependence and poor cross-region migration, supports regional modeling, and balances segmentation accuracy and efficiency, thereby providing a new technical solution for complex terrain region agricultural remote sensing application.

Phase gradient metasurface surface plays a crucial role in wireless and satellite communication, radars and remote sensing applications. However, conventional approaches for obtaining high gain of an incident wave suffer a low phase variation. The present disclosure provides a ring loaded alford-loop based phase gradient metasurface lens for x-band applications to achieve 0-360° transmission phase variation. The phase gradient metasurface lens of present disclosure includes a two-dimensional periodic array of a plurality of unit cells arranged as a M*N matrix along x-axis and y-axis. Each of the plurality of unit cells is a four layered slot typed structure with a periodicity. Each of a plurality of unit cell layers in the four layered slot typed structure comprises a modified alford-loop structure with four L-shaped arcs and it is enclosed by an outer square ring. The four layered slot typed structures are identical and separated by an air gap.

The application discloses a forest fire situation analysis method and device based on satellite remote sensing and a medium, and belongs to the technical field of remote sensing application. The method comprises the following steps: collecting multispectral satellite remote sensing image data of a region to be researched, and preprocessing the multispectral satellite remote sensing image data to obtain a multispectral satellite orthographic image data set; in the case that a fire occurs in the region to be researched, performing false color band synthesis on the multispectral satellite orthographic image data set to obtain a false color image, and extracting a burned pixel from the false color image to determine a forest fire line contour; calculating an area in the forest fire line contour based on Arcgis software, and calculating a difference value of a vegetation normalized index before and after a disaster based on ENVI software to evaluate a fire intensity. The application realizes the identification of a forest fire through an efficient and accurate method, and timely risk evaluation after the occurrence of the fire.