150 results about "Radiometric correction" patented technology

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention relates to the technical field of data processing, and discloses a seawall feature information extraction method and system based on multi-source remote sensing data. The method comprises the following steps: carrying out wave band combination enhancement, geometric correction and radiation correction on a multi-source remote sensing image to obtain standardized data; extracting a seawall target by using multi-scale segmentation; recognizing structural features through profile analysis and spectrum matching; calculating vegetation indexes and texture features to obtain ecological information; establishing a precision evaluation system; and finally constructing a feature information database. Through the technical means of multi-source data fusion processing, multi-scale feature extraction, ecological feature evaluation and the like, the precision and efficiency of seawall feature information extraction are improved, and the problems existing in the aspects of data acquisition, processing and evaluation in a traditional method are solved.

The invention relates to the technical field of gas leakage detection, and discloses an infrared imaging radiation correction method and system for gas leakage detection, and the method comprises the steps: obtaining the distribution data of dark current noise, and the mapping relation parameters of an image gray value and a radiation brightness value; acquiring infrared radiation of the gas leakage scene by using an infrared detector to obtain an original infrared image; based on the distribution data of the dark current noise, performing pixel-by-pixel compensation on the original infrared image to generate an infrared image after dark current correction; extracting the radiation intensity distribution of the infrared image after dark current correction, calibrating the radiation value of each pixel in the infrared image based on the mapping relation parameter between the image gray value and the absolute radiation brightness value, converting the image gray value into the absolute radiation brightness value, and generating the infrared image after radiation calibration; and outputting an infrared image after radiometric calibration for subsequent gas leakage area identification and feature analysis. According to the invention, a high-quality and quantifiable radiation quantification image can be output.

The present invention relates to the technical field of algae content monitoring. Disclosed is an algae content information monitoring method based on multi-source remote sensing data. The present invention comprises: acquiring multi-source remote sensing data comprising satellite remote sensing data, unmanned aerial vehicle remote sensing data and ground monitoring data, and preprocessing the data, involving radiation correction, geometric correction and noise elimination; and fusing the multi-source data into a trained algae monitoring model for prediction, and using Kalman filtering to assimilate observation data and model prediction data. The present invention fuses observation data and model prediction data by means of Kalman filtering technology, so as to dynamically adjust the model state, such that the model can more accurately reflect the actual observation situation. The Kalman filtering optimizes the real-time prediction capability of models by balancing the uncertainties between observation data and prediction data, thereby ensuring the accuracy and real-time performance of monitoring results, and also ensuring the reliability and adaptability of models under various environmental conditions.

The invention discloses a multi-source remote sensing and unmanned aerial vehicle combined wetland monitoring method, equipment and a medium, relates to the technical field of wetland monitoring, and realizes efficient and accurate wetland monitoring through combination of satellite remote sensing and unmanned aerial vehicle technologies. Firstly, space-time continuity and noise suppression preprocessing is carried out on a satellite remote sensing image, and the image quality is improved; thirdly, the image is optimized through segmentation fusion processing, and the species distribution range and the community boundary are accurately extracted; the unmanned aerial vehicle monitoring path is planned based on the data, and the monitoring efficiency is improved. A high-resolution multispectral image obtained by the unmanned aerial vehicle is subjected to radiation correction and geometric correction and then is fused with a satellite remote sensing image to generate a high-precision wetland species distribution diagram. According to the scheme, the advantages of large-range coverage of satellite remote sensing and high resolution of the unmanned aerial vehicle are fully played, the defects of data quality, information extraction and monitoring efficiency in a traditional method are overcome, and powerful support is provided for wetland protection and management.

The invention relates to the technical field of SAR (Synthetic Aperture Radar) image processing, and particularly discloses an SAR image terrain radiation correction method considering overlay and shadow regions, which comprises the following steps: acquiring an SAR image, a DEM (Digital Elevation Model) image and a source file in a research region; carrying out multi-view processing on the original SAR image, resampling the DEM image, and carrying out radiometric calibration; geometric positioning is carried out according to the RD model by adopting an indirect positioning method, lookup tables A and B are generated, and images are registered; detecting pixels of the overlay area and the shadow area; calculating an effective scattering area corresponding to each SAR pixel; and performing radiation correction according to the effective scattering area, repairing a back scattering coefficient value of the shadow area, and finally performing geometric correction. According to the method, the topographic effect caused by an SAR imaging mechanism and topographic relief is eliminated to a certain extent, the radiation correction effect of an area with large topographic relief is improved, and the corrected product can be widely applied to the fields of ground feature inversion and classification, deformation detection, target detection and the like.

The invention provides a correction method and system for anisotropic reflection of GF6-WFV data, and belongs to the technical field of spatial information.The correction method comprises the steps that the GF6-WFV data is subjected to block processing, data of a block center is compared with data extracted from satellite navigation file data, atmospheric correction is carried out on the block data one by one, and the correction efficiency is improved; the processing efficiency and the correction precision are remarkably improved, image data are divided into small blocks with 2000 * 2000 pixels through blocking processing, the pixel amount calculated each time is reduced, the method is more suitable for rapid processing of large-data-amount and high-resolution images, the blocking coverage range is large, local and overall consistency can be taken into account, and secondly, the processing efficiency and the correction precision are greatly improved. And the zenith angle and observation angle parameters of each block are standardized by the angle normalization model, so that the angle difference between different areas is reduced, and the accuracy of a correction result is improved. And finally, in combination with a lookup table technology, computing resource consumption can be remarkably reduced, and the method is convenient to operate in practical application of radiation correction and has the advantages of high efficiency and convenience.

The invention discloses a method for analyzing a climate change rule based on remote sensing data. Multi-source remote sensing data are obtained through multiple platforms of satellites, aviation and ground, and after preprocessing of geometric correction, radiation correction and the like, features of surface temperature, humidity and the like are extracted through a split window algorithm, a polarization difference index method and the like. Principal component analysis is used for dimensionality reduction, partial least squares regression is used for determining the quantitative relation between features and climate changes, and a correlation model is established by considering human activity influences. The method comprehensively obtains data, improves the processing and analysis precision, establishes the relation between human activities and climate changes, can accurately determine the climate change rule, and provides powerful support for climate change research and response.

The invention discloses a leakage gas cloud three-dimensional reconstruction method, and relates to the technical field of gas cloud image processing. According to the leaked gas cloud three-dimensional reconstruction method, through combination of a multispectral image data cube and a pre-trained Yolov5 model, precise identification and analysis of a gas cloud leakage area are realized, through radiation correction and adaptive filtering processing, absorbance data of the gas cloud are extracted and fitted, concentration distribution of each layer of gas cloud is further obtained, and the three-dimensional reconstruction of the leaked gas cloud is realized. By stacking data of each layer and applying a three-dimensional reconstruction algorithm, an accurate gas cloud three-dimensional structure is generated, high-precision spatial distribution information is provided for gas leakage monitoring, the monitoring and response efficiency is remarkably improved, the limitation of a traditional method in the aspects of precision and adaptability is overcome, and by integrating multi-view and multi-band image data, the real-time performance of gas leakage monitoring is improved. The gas cloud leakage area can be identified more accurately, and the gas cloud concentration distribution is accurately obtained through asymmetric Gaussian function fitting, so that the gas leakage monitoring precision is improved.

The invention provides an interpretable random forest surface temperature downscaling method coupled with meteorological factors, and relates to the technical field of space downscaling. The method comprises the following steps: acquiring low-resolution surface temperature remote sensing data and multi-source feature data of a target area, and performing radiometric calibration, unified projection and normalization to form a preprocessed data set; dividing earth surface type subareas according to the earth surface classification map, solving a radiation correction coefficient by combining historical meteorological statistics, and performing subarea-level correction to obtain a subarea correction data set; training a random forest regression model by using the partition correction data set, and establishing a nonlinear mapping relationship between the surface temperature and the multi-source features; inputting high-resolution feature data to deduce a preliminary high-resolution surface temperature, and performing residual interpolation correction to obtain a final result; and the SHAP algorithm is adopted to explain feature contributions, so that the result precision, stability and interpretability are improved.

The invention provides a multi-modal alignment and cross-scale structure attention-based remote sensing image building extraction method, which comprises the following steps of: performing radiation correction and geometric correction on an original hyperspectral satellite image, and performing voxelization, normalization and registration with a hyperspectral corrected image on LiDAR point cloud data to respectively obtain the hyperspectral corrected image and LiDAR corrected data; deep features of the two types of correction data are extracted through a special encoder, and after dimension unification and channel attention screening, fusion features are obtained through fusion of a weighting and gating mechanism; multi-scale features are extracted from the fusion features through multi-receptive field convolution branch, and cross-scale enhancement features are obtained by combining cross attention and geometric modeling; a prediction edge graph is generated based on cross-scale enhancement features, high-resolution guide features are combined to compensate tiny building information, and the extraction precision is improved through edge loss optimization; and carrying out denoising, edge repairing and adhesion separation on the optimized result to obtain a final building extraction result.

The invention relates to the technical field of agricultural quantitative remote sensing and intelligent agriculture, and discloses a multi-source remote sensing data radiation correction and chlorophyll content cross-platform inversion method, which comprises the following steps: synchronously obtaining ground actual measurement, unmanned aerial vehicle and satellite remote sensing data, calculating vegetation indexes, and screening sensitive characteristic variables by using Pearson correlation analysis; establishing a radiation correction model by adopting a ratio averaging method, performing radiation normalization processing on satellite data by taking unmanned aerial vehicle data as a reference, and eliminating sensor response differences; an SPAD inversion model is constructed and optimized based on Z-score standardization, the SPAD inversion model is migrated and applied to corrected satellite data, a regional scale chlorophyll distribution diagram is generated through pixel-by-pixel calculation, and then a differentiated farmland management strategy is formulated. According to the method, the difficulty of multi-source data collaboration is solved, the regional scale crop chlorophyll monitoring precision is improved, and scientific decision support is provided for large-area crop fertilization.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

The invention provides a winter wheat soil moisture inversion method and system based on an improved water cloud model, storage equipment and electronic equipment, and the method comprises the following steps: obtaining total backscattering characteristics VV and VH, an incident angle and a plurality of polarization characteristic parameters through employing a single-view complex image and a geocoding radiation correction image; calculating a vegetation index from the preprocessed optical image by applying a wave band calculation mode; an improved water cloud model method is applied to correct the radar backscattering coefficient affected by the winter wheat plants, the backscattering coefficient of the earth surface is obtained, and data redundancy is reduced; performing hyper-parameter optimization of the CNN network by applying a CPO algorithm; and constructing a CNN-CPO-RF model by using CNN and RF models to carry out soil moisture estimation. According to the invention, a more accurate soil moisture result can be obtained. According to the method, the robustness of farmland surface soil moisture inversion is remarkably improved, and a new technical means is provided for performing soil moisture inversion by using a satellite-borne SAR image.

The invention provides a satellite observation data restoration method and device based on generation of a collaborative adversarial network, and relates to the technical field of data restoration, and the method comprises the steps: extracting a cloud coverage sub-region and a cloud-free sub-region from a target region, and carrying out the cloud coverage sub-region and the cloud-free sub-region based on respective corresponding current satellite observation data subsets; performing space-time registration and radiation correction on the historical satellite observation data subset corresponding to the cloud coverage sub-region under the condition of no cloud coverage to obtain a corrected historical satellite observation data set corresponding to the target region; and performing progressive cooperative training on the generated cooperative adversarial network by using the current satellite observation data set of the target area and the corrected historical satellite observation data set. According to the method, the problems of poor cloud coverage data missing repair effect and insufficient physical consistency in a traditional method can be relieved.

The invention provides a satellite-borne synthetic aperture radar in-orbit radiation correction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining echo data of a synthetic aperture radar, and obtaining a first antenna pattern; preprocessing the echo data to obtain preprocessed echo data; performing compression processing on the first antenna pattern to obtain a two-dimensional lookup table; constructing a comprehensive compensation factor according to the two-dimensional lookup table; introducing the comprehensive compensation factor into a pre-constructed first imaging model to obtain a second imaging model; and inputting the preprocessed echo data into the second imaging model to obtain target echo data. According to the invention, the echo data of the synthetic aperture thunder is corrected before imaging, the processing flow is simplified, the consumption of computing resources is reduced, the time consumption is shortened while the precision is improved, and the imaging efficiency is improved.

The invention belongs to the technical field of image processing, and particularly relates to a multi-source data-driven flood storage and detention area ecological toughness detection method and system, and the method comprises the steps: 1, carrying out the geometric correction and radiation correction of an optical satellite remote sensing image, and forming a time sequence basic data set; 2, a first axis and a second axis of the ecological state matrix correspond to space grids of the flood storage and detention area respectively, and a third axis corresponds to time; 3, mapping the ecological state matrix to a fractal space to form a time coupling matrix; 4, for each time slice of the time coupling matrix, setting a neighborhood with a fixed size around each pixel, and performing time window segmentation and fractal complexity estimation on the multi-scale morphological gradient map; and step 5, calculating the toughness value of the connected region according to the area and the toughness values of all positions in the connected region. According to the method, the accuracy and practicability of ecological toughness detection are remarkably enhanced.

The invention relates to the technical field of synthetic aperture radar signal processing, and discloses a satellite-borne SAR image in-orbit radiation and geometric correction method based on a parallel computing architecture, and the radiation correction method comprises the steps: obtaining ephemeris parameters of a satellite under a geocentric coordinate system at all azimuth moments; calculating a slant distance between a satellite beam center pointing scene center point and an antenna phase center at each azimuth moment and a slant distance between each sampling point and a satellite in an SAR echo distance direction at each azimuth moment; calculating satellite beam center pointing at each azimuth moment and a lower visual angle between the satellite and a sub-satellite point connecting line; calculating an antenna distance direction beam directional diagram and constructing a radiation correction function; and performing parallel correction on radiation distortion of the SAR image based on the radiation correction function. According to the method, the SAR image quality and the accurate positioning of the SAR image are effectively improved, the radiation fidelity of the SAR image is met, meanwhile, the efficiency of a data processing task is improved, and the task requirement for high real-time performance is considered.

The invention discloses a film thickness measurement method and system based on a line scanning hyperspectral camera, and relates to the technical field of hyperspectral imaging, and the method comprises the steps: obtaining a reflection or transmission spectrum image and a dark noise image, and carrying out the denoising processing; using a hyperspectral camera and an integrating sphere to carry out radiation correction on the reflected or transmitted spectral image; acquiring a film reflectivity or transmissivity spectrum according to the transmission spectrum without the film or the standard specular reflection spectrum; and determining the number of global interference periods according to a reflectivity or transmissivity curve, and calculating by using a fast Fourier transform method or a spectrum fitting method to obtain the film thickness. Image data with space and spectrum information is acquired through a hyperspectral camera, high-precision film thickness measurement under global imaging and dynamic scanning is realized through an algorithm combining fast Fourier transform and spectrum fitting, and meanwhile, the film thickness measurement precision is improved by adopting dark noise correction and integrating sphere radiation correction. The quantitative accuracy of incident light and sample reflection or transmission light is ensured, and the detection efficiency is greatly improved.

The invention provides a building change detection method and system based on a twinborn change attention residual network. The method comprises the following steps: acquiring original remote sensing images of two stages of research areas; performing geometric registration and radiation correction on the original remote sensing image in sequence to obtain two time-phase feature maps; and inputting the two time phase characteristic graphs into a twinborn change attention residual network based on ResNet and UNet to obtain a change probability graph. The scheme provided by the invention has higher robustness and accuracy in a building change detection task.

The invention discloses an image real-time radiation correction method for a push-broom hyperspectral camera, and relates to the technical field of image processing methods. The method comprises the following steps: on the basis of an established real-time radiation correction system, defining acquisition parameters, and collecting hyperspectral data of four standard reflectivity plates by using a hyperspectral camera; the hyperspectral image and spectrograph information are aligned through a timestamp, and an ROI region method is established to complete image preprocessing of the push-broom hyperspectral camera; the information collected by the spectrometer is corrected by correcting the azimuth angle of the cosine corrector, and a hyperspectral correction model is established to predict the reflectivity. According to the method, data acquisition is accurate, real-time radiation correction of the hyperspectral image can be realized, and the correction precision is high.

The invention provides a multi-data combined construction site three-dimensional model construction method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the calling of a preset resolution satellite image, and obtaining a full-region stereo image pair data set; after radiation correction is carried out, a region surface model and a region elevation model are generated; grid-level modeling defect detection is carried out, and distributed blind area blocks are positioned; performing obstacle avoidance path fitting to generate an obstacle avoidance optimized flight path; the unmanned aerial vehicle is driven to execute multi-angle surrounding scanning, and distributed blind area dense point clouds are directionally collected; constructing a distributed blind area model; and carrying out microscopic superposition, and outputting a three-dimensional model of the construction site. The technical problems that a construction site modeling method in the prior art is limited in the aspects of coverage, modeling efficiency and data real-time performance, depends on a single data source, cannot provide a comprehensive and accurate three-dimensional model of a construction site, and affects the construction progress and decision are solved.

The invention provides a sea reclamation change remote sensing image target classification method and system, and the method comprises the steps: obtaining a remote sensing satellite image of a monitored sea reclamation region, carrying out the geometric correction, radiation correction and tide level normalization correction of the remote sensing satellite image, and carrying out the fusion of the corrected remote sensing satellite images; a fused remote sensing satellite image is cut into training slices for training, an MCFCN model is optimized, test slices are predicted, and CRF correction and morphological corrosion post-processing and splicing are carried out to form a final result. Through the method and the corresponding system, the segmentation efficiency and the detection accuracy of the sea reclamation change remote sensing image can be improved.

The invention relates to the technical field of remote sensing monitoring and climate analysis, in particular to a high-altitude area surface albedo remote sensing monitoring method and device, and the method comprises the steps: firstly obtaining the spectrum, infrared and topographic data of the surface, carrying out the radiation correction and topographic feature extraction, and generating preprocessing data; thirdly, generating multi-dimensional albedo data by using a multi-level fusion technology and an albedo calculation model, and obtaining joint albedo data through frequency domain and time domain analysis; and finally, generating a high-precision albedo product through a self-adaptive deep learning model and a residual error correction mechanism, and applying the high-precision albedo product to climate monitoring, ecological assessment and disaster early warning of a high-altitude area. According to the method, the accuracy and temporal-spatial resolution of albedo data are effectively improved, and reliable data support is provided for environmental research of high-altitude regions.

The invention discloses an irregular topographic surveying and mapping system and method based on a remote sensing technology, belongs to the field of topographic surveying and mapping, and solves the problem of poor precision and accuracy of irregular topographic surveying and mapping in the prior art. Reliability and accuracy of remote sensing data can be ensured, geometric correction and radiation correction are performed on the remote sensing data through the data processing unit, distortion and errors can be eliminated, accuracy of the remote sensing data can be improved, then extracted terrain data are transmitted to the terrain analysis unit, and data transmission state and channel transmission quality are monitored during transmission. The reliability, the safety and the efficiency of data transmission are effectively improved, and the terrain analysis unit performs data analysis and visual display on the terrain data so as to complete surveying and mapping work on irregular terrains and facilitate visual understanding and explaining of terrain features.

The invention relates to the technical field of data fusion, and particularly provides a multi-scale eco-hydrological parameter joint inversion method and system based on unmanned aerial vehicle and satellite-ground data fusion, and the method comprises the steps: obtaining multispectral, hyperspectral and thermal infrared data of a research region through a high-resolution sensor carried by an unmanned aerial vehicle; the method comprises the following steps: acquiring unmanned aerial vehicle data, acquiring satellite remote sensing data, performing radiation correction on the unmanned aerial vehicle data and the satellite data, and performing feature extraction and space-time fusion on the unmanned aerial vehicle data and the satellite data by using a deep learning algorithm to generate high-resolution fusion data; through a multi-resolution analysis or spatial interpolation technology, coordinating differences of unmanned aerial vehicle data and satellite data in space and time scales, and constructing a multi-scale data set; based on a physical model and a data driving model, combined with fusion data, an inversion result is visually displayed, and the method has the effects of meeting the hydrological monitoring requirement of high time resolution, and particularly providing real-time monitoring data in the hydrological process of rapid change.

The invention provides a dry-farming oat growth vigor monitoring method and system based on unmanned aerial vehicle hyperspectral remote sensing, and relates to the technical field of dry-farming oat growth vigor monitoring. The reflectivity of each image pixel under different spectral band conditions is calculated through the absolute radiation brightness and the known reflectivity of the reference plate, the average reflectivity of the unit grids in different spectral bands is obtained, a linear product is adopted to construct a growth score index, the growth score index of each grid unit is clustered by adopting a K-means clustering method, and the average reflectivity of each grid unit is calculated. And outputting the clustering condition of the growth vigor of the dry farming oats of the grid units, and setting a judgment threshold according to the clustering condition of the growth vigor of the dry farming oats to realize growth vigor classification. A reference plate is introduced to calibrate the reflectivity, the pixel-level absolute reflectivity is calculated through a radiation transfer equation, and the influence of illumination condition changes is eliminated; k-means clustering is adopted to automatically divide growth vigor levels, and a threshold value can be generated in a self-adaptive mode based on data distribution.

The invention relates to a fertilization prescription map rapid generation method and system based on multispectral detection, and belongs to the technical field of agriculture. Comprising the following steps: multi-source data acquisition: synchronously acquiring a 450-900nm wave band crop multi-spectral image acquired by a multi-spectral camera carried by an unmanned aerial vehicle, laboratory detection nutrient data of a preset soil sampling point, real-time soil humidity data, plot boundary vector data and a crop recommended fertilization model; data preprocessing: carrying out radiation correction, geometric correction and de-noising processing on the multispectral image; performing crop nutrient demand inversion; according to the method, the multi-spectral image of the unmanned aerial vehicle, the soil sampling data, the real-time soil humidity and the plot boundary vector data are synchronously integrated, a raster data set of a unified coordinate system is constructed, and crop nutrient requirements and environment variables are comprehensively covered. In combination with vegetation index combinations such as NDVI, RENDVI and RVI, the crop nutrient content is inverted through the PLSR algorithm, and the prediction precision R2 is greater than 0.85 and is significantly superior to that of a single index model.

The invention discloses an electric power gallery flood risk assessment method and system based on deep learning, and belongs to the field of flood disasters, and the method comprises the steps: achieving the water extraction and risk grade assessment of a radar image, obtaining the radar image from a Sentinel-1 satellite, removing the noise through a Speckle filter, and carrying out the geometric correction, radiation correction and normalization processing. Data preprocessing is carried out to ensure data quality; performing image segmentation on the preprocessed image by using a U-Net model, extracting and recovering image features through an encoder and decoder structure, and realizing high-precision water body segmentation through a deep learning model; the water body extraction precision is evaluated by calculating indexes such as a confusion matrix, a recall rate, accuracy and MIOU, the flood risk level of the power equipment is evaluated according to the extracted water body data, the gallery bridge distance and the gradient, and risk evaluation is divided into five levels. According to the method, the problem of how to accurately extract the water body and evaluate the flood risk of the electric power gallery is solved, and a scientific basis is provided for protection and management of electric power facilities.

The invention relates to the field of agriculture, and discloses a method for evaluating sugarcane smut based on an unmanned aerial vehicle image, which comprises the following steps: data acquisition: planning a route by using an unmanned aerial vehicle carrying a multispectral camera, a hyperspectral camera and an RGB (Red, Green and Blue) camera, so that the unmanned aerial vehicle acquires image data of sugarcane; preprocessing: sequentially carrying out geometric correction, radiation correction and image fusion processing on the image data to obtain preprocessed data; feature fusion: constructing a fusion model including spectral feature extraction, visual feature extraction and spatial-temporal feature fusion according to the feature expression of the sugarcane smut; and model generalization: collecting multi-source images and disease investigation data according to the actual condition of the sugarcane. The method comprises the following steps: performing data acquisition through an unmanned aerial vehicle, obtaining multi-dimensional image data in a sugarcane growth critical period, performing preprocessing, constructing a fusion model including spectral feature extraction, visual feature extraction and spatial-temporal feature fusion, performing fine adjustment and optimization in combination with a transfer learning strategy, and performing real-time analysis on a disease detection result.