722 results about "Remote sensing image processing" patented technology

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a remote sensing image building change detection method, which comprises the following operation steps: (1) reading an image and preprocessing the image; (2) making a sample data set; (3) constructing a network model based on an attention mechanism and a feature pyramid; (4) selecting a training sample to train the network model; (5) selecting a verification sample to verify the network model; and (6) classifying by using the trained model, and outputting a final change detection result. According to the method, a feature pyramid network is introduced, an attention mechanism is used in the multi-scale feature fusion process of all levels, and features are enhanced layer by layer tobe used for target detection of different scales; through application of deformable convolution and cavity convolution, the network obtains the feature expression capability of automatically adaptingto object deformation, the feature size is reserved while the receptive field is expanded, multi-scale information is obtained, the false alarm rate is effectively reduced, and the detection precisionis improved.

The invention discloses a method for conducting high-precision and steady registration on diversified sensor remote sensing images, which relates to the field of remote sensing image processing and adopts the following steps aiming at the problem that the control points are unevenly distributed in the registration process: step 1: crude registration of combined point characteristics and regional characteristics and elimination of larger scale, rotation and translation differences between a reference image and an input image, which are realized by matching the combined point characteristics andregional characteristics; step 2: scale space feature extraction and matching of a great amount of registration control point pairs, which aim at extracting a great amount of control point pairs forfine registration; and step 3: screening and fine registration based on control point information amount, which aims at screening the control points according to self-contained information amount andfinishing high-precision fine registration. The invention provides the method for conducting high-precision and steady registration on diversified sensor remote sensing images.

The invention discloses a hyperspectral image classification method based on deep learning, and belongs to the technical field of remote sensing image processing. The method comprises the steps that 1, dimension reduction treatment on a hyperspectral image is achieved by obtaining a data sample, conducting layer-by-layer training on an autoencoder network and further adjusting an initial weight value obtained through pre-training by adopting a BP algorithm; a data cube in each pixel neighbourhood in the hyperspectral image is taken as input of a convolutional neural network, a ground object type corresponding to a pixel serves as expected output of the convolutional neural network, the convolutional neural network is trained, the trained convolutional neural network acts on the whole hyperspectral image, and a final high-precision classification result is obtained. According to the method, the defects that in a traditional hyperspectral image classification problem, details are discarded in the dimension reduction process, space information is lost in the classification process, and the classification precision is low are overcome, the good classification precision is achieved, and the method is suitable for classification of various hyperspectral images.

The invention discloses a monitoring device and a method for crop growth uniformity degree, aiming at the problem of low efficiency for manually investigating data. The device comprises a remote sensing image processing module, a plot vector data processing module, a vegetation index processing module and a growth uniformity processing module. The method comprises: obtaining the remote sensing images of a satellite; carrying out radiometric ratification, atmospheric correction and geometric correction according to the remote sensing images of the satellite; classifying crops in the remote sensing images of a satellite to obtain a spatial distribution map; converting a grid classification result in the spatial distribution map into facet vector data; processing the spatial distribution map to correct the plot boundary of the crops; according to the spectral characteristics of the plot in the remote sensing image, calculating the vegetation index of the plot; and calculating the growth uniformity degree index according to the vegetation index of each plot. The invention uses the integration of the grid and the vector data to monitor the growth uniformity degree of crops in natural plots and performs the advantages of remote sensing data.

The invention belongs to the technical field of remote sensing image processing, and specifically relates to a low-rank expression and learning dictionary-based hyperspectral image abnormity detection algorithm. According to the algorithm, a method for introducing low-rank expression in the abnormity detection problems is used for decomposing the two-dimensional hyperspectral image data into the sum of a low-rank matrix expressing background and a sparse matrix expressing abnormity, and then enabling a basic abnormity detection algorithm to act on the sparse matrix to obtain the abnormity detection result; and furthermore, the concept of a learning dictionary is imported in the low-rank expression algorithm, and the learning dictionary is obtained through an algorithm of random selection and gradient descent and is capable of expressing the background spectrums in hyperspectral images. Through the importing of the learning dictionary, the abnormity information can be better separated from the hyperspectral image data, so that better detection result can be obtained; and meanwhile, the robustness of the algorithm for the initial parameters can be improved, so that the computing cost is reduced and important value is provided for the actual abnormity detection application.

The invention relates to a method for segmenting a remote sensing image and belongs to the technical field of processing of the remote sensing image. The method comprises the following steps of: according to color feature, textural feature and the position information of a pixel point, generating a feature vector which serves as an initial input vector; segmenting the remote sensing image by using mean shift filtering; and adaptively determining weights of the color feature and the textural feature by using constructed cluster validity indexes to effectively improve the segmenting accuracy ofthe remote sensing image. The invention also discloses a method for searching the remote sensing image by using the method for segmenting the remote sensing image. The search of the remote sensing image is finished by using a new region representation and matching method. Compared with the prior art, the invention has higher search accuracy, particularly for search of a small target region.

The invention belongs to the technical field of remote sensing image processing, and particularly discloses a Fisher judged null space based method for decomposing mixed pixels of a high-spectrum remote sensing image. A Fisher judged null space method is provided aiming at the problem that the decomposition precision is reduced due to the phenomenon of same objects and different spectrums generally existing in the mixed pixel decomposition. The method comprises the following steps: analyzing a training sample consisting of pure pixel spectrums of an end member, constructing an intra-class scattering matrix null space of the training sample, making the spectrum difference in the end member become null, searching a judgment vector causing the scattering degree of the intra-class scattering matrix to be maximum in the null space, and making the separation degree of end member spectrums of different classes maximum so as to furthest reduce the decomposition error caused by the same objects and different spectrums. The method of the invention has particularly important application values in the aspects of high-precision surface feature decomposition of the high-spectrum remote sensing image and detection and identification of ground targets.

The invention discloses a context-constraint-based target identification method, and belongs to the field of remote sensing image processing. The method is used for classifying a remote sensing image scene and detecting and identifying a target. The method comprises the following steps of: filtering an image, performing region segmentation to segment the image into a plurality of connected regions, and marking each connected region; calculating characteristic vectors of each connected region, inputting the characteristic vectors into a classifier which is trained in advance for scene classification calculation, and outputting a class mark sheet; determining the scope of a local region in which a target possibly exists on the mark sheet according to the target to be identified on the basis of the class mark sheet, preprocessing the local region, and calculating a region of interest in the region; and extracting characteristics, and inputting the characteristics into the classifier for identification. The scene classification method is quick and effective, and aims to provide effective context constraints for target identification and improve the identification efficiency and the identification accuracy.

The invention discloses a remote sensing image processing method combined with the shape self-adaption neighborhood and the texture feature extraction for image preprocessing. The method includes subjecting compressed image to a gray level co-occurrence matrix calculation; subjecting the generated gray level co-occurrence matrix to S coefficient modification of an SAN (Storage Area Networking) irregular object window to obtain a regular matrix; calculating a new co-occurrence matrix according to the modified regular matrix and selecting texture descriptors with obvious feature and low correlation; extracting texture feature map in the SAN irregular images; and calculating to obtain accurate images with combination feature which is overall comprehensive feature of neighborhood. According to the method, the overall classification accuracy based on a shape self-adaption neighborhood method can be improved by 4%. The method can not only extract the texture feature in the SAN irregular images of remote sensing images completely, but also process the extraction of mixed pixel feature of the fuzzy edge of earth surface objects, and is applicable to texture extraction of earth surface objects in natural states.

The invention belongs to the technical field of remote sensing image processing and in particular relates to a manifold dimension reduction method of hyperspectral images based on image block distance. According to the method, a novel distance measure, namely an image block distance measure is provided and is applied to neighborhood selection and lower dimension coordinate embedding in manifold learning, and a nonlinear dimension reduction method of novel hyperspectral remote sensing images is obtained. The physical properties of the hyperspectral images are utilized, the spectral information and space information of the images are combined, the local characteristics between data points can be better kept, and the characteristics of an original data set are well kept on the basis of reducing the image information redundancy to the greatest degree. The method has high applicability on various different hyperspectral data, and has important application values in detection and identification aspects of high-precision terrain classification and ground targets based on the hyperspectral remote sensing images.

The invention discloses an automatic geometric correction and orthorectification method for multispectral remote sensing satellite images of a mountainous area. The method comprises the following steps of: selection and splicing of reference images; automatic selection of object points with same names; screening of the object points with same names, and geometric correction and orthorectification for correcting the images; precision evaluation; and output of the images. The multispectral remote sensing satellite images of the mountainous area, such as images of China domestic environment and disaster monitoring small satellite (with the model HJ-1A / B), have the characteristic of serious geometric distortion, but the automatic geometric correction and orthorectification method can be used for performing the orthorectification on the multispectral remote sensing satellite images of the mountainous area in mass and generating residual error reports of the coherent images by adopting a concept of the object points with same names to implement the geometric correction and orthorectification. The automatic geometric correction and orthorectification method has the advantages of high efficiency and precision, is very efficient when being used for processing the multispectral remote sensing satellite images with mass data of the mountainous area, can remarkably save manpower and material resources needed by the conventional remote sensing image processing process, and particularly is beneficial to the processing of the images of the China domestic environment and disaster monitoring small satellite.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method based on neighborhood similarity and mask enhancement, relating to the field of remote sensing image processing. The method mainly solves the problem that a difference map construction of SAR image change detection is seriously influenced by SAR image speckle noise and comprises the following steps of: (1) geometrically correcting and registering two SAR images I1 and I2 in the same scene at different times; (2) constructing a difference image map DI of the images I1 and I2 by using a neighborhood similarity operator; (3) performing mask enhancement on the difference image map DI to obtain a new difference image map NDI; and (4) clustering the new difference image map NDI to obtain a change detection result map CDI by using a K mean value clustering method determined by an initial point. The method has the characteristics of high operation efficiency, low algorithm complexity, low time complexity and good experimental effect, and can be used for the change detection of two time-phase SAR images.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method based on a neighborhood logarithm specific value and anisotropic diffusion, relating to the field of remote sensing image processing and mainly solving the problem that a difference graph structure of SAR image change detection is seriously influenced by SAR image spot noises. The SAR image change detection method comprises the following steps: (1) structuring a difference striograph IL of two images I1 and I2 of different times and same terrain according to a neighborhood logarithm specific value method; (2) carrying out self-adaptation window anisotropic diffusion filtering processing on the difference striograph IL to obtain a final filtering result graph NI<t>[L] of the difference striograph; and (3) carrying out threshold segmentation on the final filtering result graph NI<t>[L] of the difference striograph by using an OSTU (Maximum Between-Class Variance) threshold algorithm to obtain a change detection result graph CNI<t>[L] for structuring the difference striograph by using the neighborhood logarithm specific value method. The histogram of the difference striograph can be compressed so as to effectively eliminate miscellaneous points in the change detection result graph; and the self-adaptation window anisotropic diffusion filtering has favorable edge retentiveness and cannot blurs the edges of the image, thus, an obtained change detection result graph is finer.

The invention discloses a method for generating a remote sensing image quick view under Linux environment and belongs to the technical field of remote sensing image processing. The method comprises that (1) an image of a band sequence (BSQ) format is generated to an image of a tagged image file (TIF) format and (2) a quick view file of a joint photographic experts group (JPEG) format is generated, wherein the step (1) comprises of a, linear mapping, b, pretreatment of decimal part of image data, c, cutting of image data in accordance with a threshold, d, histogram equalization and e, generation of an image file of the TIF format. The method has the advantages of being simple and easy to operate. The generated quick view can describe data information of a current wave band of a hyperspectral image accurately, the method is used for processing 8-position unsigned char type data converted from 32-position single precision floating point type data, and the quantification method has low noise.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a new mixed pixel decomposition method based on an NMF (non-negative matrix factorization) algorithm. The method comprises the following steps: introducing constraint conditions for abundance separability and smoothness into the target functions of the NMF algorithm according to the spectrum and abundance characteristics of hyperspectral images; and removing the constraint conditions at the right time, and continuing to carry out iteration, thereby overcoming the defects that the NMF algorithm is easily sunk into local minimum, so that the mixed pixel decomposition method for high mixed remote sensing data can be implemented effectively. The method disclosed by the invention has especially important application value in the aspects of detecting and identifying ground targets and classifying topographical objects based on the high accuracies of multispectral and hyperspectral remote sensing images.

The invention belongs to the remote sensing image processing technical field, in particular to a remote sensing image mixed pixel decomposition method based on self-organization mapping neural network. The method integrates the self-organization mapping neural network and the fuzzy grade of membership in the fuzzy theory to work out abundance values after decomposition. Simultaneously, as the self-organization mapping neural network does not has the competitive learning characteristic of an objective function, the method is free from the problem of local extremum. In addition, the invention automatically meets the two bindings required by the mixed pixel decomposition problem, wherein the two binds are respectively the binding of non-negative abundance values and the binding of the sum of the abundance values being 1. The invention has a better mixed pixel decomposition effect and a higher anti-noise capacity. The new method has a particularly important application value concerning the high-accuracy ground object separation and ground target detection and identification based on multi-spectrum and high spectrum remote sensing image.

A method for restoring hyperspectral remote sensing image based on non-convex and low-rank sparse constraint belongs to the field of hyperspectral remote sensing image processing in remote sensing image processing. In order to solve the problem that the existing hyperspectral remote sensing image restoration technology can not effectively remove noise and improve the image restoration quality, themethod comprises the following steps: inputting a hyperspectral remote sensing image; initializing a weight coefficient matrix, iterative times and a convergence threshold, initializing sub-image size and scanning step, partitioning sub-blocks; establishing an image restoration model; the auxiliary variable and the coefficient of the regular term being introduced, and the maximum-minimum algorithm being used to solve the problem iteratively; judging whether the restoration result satisfies the convergence condition; obtaining a hyperspectral restored image that meets the requirements by iterative times, otherwise returning to corresponding steps to continue the iterative operation; calculating a weight coefficient matrix and assigning appropriate weights to each sub-block; hyperspectral remote sensing images being restored to obtain the final restored hyperspectral remote sensing images. The effect of denoising is obvious and the image details are preserved.

The invention provides a static tile pyramid parallel building method based on MapReduce, and belongs to the technical field of remote sensing image processing. The static tile pyramid parallel building method based on the MapReduce aims at fast performing batch remote sensing image parallel processing on the basis of a parallel calculation model of the MapReduce by aiming at the features of remote sensing images. The method is completed by four steps including Map tile processing, creation of Lmax layers of tiles, tile image generation and Reduce tile merging. The invention provides the improved static tile pyramid parallel building method based on the MapReduce, and the combination of fast parallel building of batch remote sensing image tile pyramid and tile pyramid large file generation is realized. Through structures of tile pyramid large files and HBase tile index tables, a fast retrieval model of global tile images is built.

The invention provides an image wavelet denoising method based on median filtering, which belongs to the field of digital remote sensing image processing. In the method, median pre-filtering is firstly performed on images containing noise, wavelet conversion, threshold value treatment and denoising treatment are then carried out, wavelet reverse conversion is finally carried out, and filtered images are output. The invention has the advantages that the median filtering method is used for the pre-filtering of images containing noise, much noise of pepper salt type is removed, and influence on the high-frequency part of images is avoided; the wavelet threshold value method is then combined with the denoising method of the relativity of wavelet coefficient size, and the mixture of signal coefficient and noise coefficient caused by using the wavelet threshold value method singly is avoided; and meanwhile, a large amount of computation required by using the denoising method of the relativity of wavelet coefficient size singly is reduced. The experiments prove that the method has stable denoising effect and moderate computation amount.

The invention provides a method for parallel processing of remote sensing imagery data. The method is based on a plurality of terminals connected through an Ethernet; at least one terminal in the plurality of terminals is provided with a task management and scheduling unit, and each terminal is provided with a computing unit. The method comprises the steps as follows: a first terminal in the plurality of terminals receives a remote image processing task; the computing unit of the first terminal resolves the remote sensing image processing task into a plurality of sub-tasks, encapsulates and submits the sub-tasks to the second terminal provided with the task management and scheduling unit; the task management and scheduling unit of the terminal issues the sub-tasks between the computing units of the plurality of terminals, and the computing units of the plurality of terminals respectively carry out the issued sub-tasks. In the invention, the computing resources of the computer used by different interpretation personnel are fully utilized, and the demand of the remote sensing image data processing on hardware of the computer is reduced.

The invention relates to a method and a device for processing a remote sensing image. The method comprises the following steps: obtaining the largest data processing amount of a plurality of subnodes in the grid; segmenting the remote sensing imaged into a plurality of remote sensing subimages according to the largest data processing amount; and sending the remote sensing subimages to the subnodes and then leading the subnodes to respectively process the received remote sensing subimages. The method and the device for processing the remote sensing image lead the remote sensing image to be segmented into the remote sensing subimages according to the largest data processing amount of the subnodes in the grid, therefore, the subnodes in the grid can parallelly process the remote sensing subimages according to the processing capacity of the subnodes, thus increasing the processing speed of the remote sensing image.

The invention discloses a frequency-domain-analysis-based method for detecting a region-of-interest of a visible light remote sensing image, belonging to the technical field of remote sensing image processing and image recognition. The method comprises the following implementation processes of: (1) preprocessing the image; (2) carrying out quaternion Fourier transformation on the preprocessed result to obtain the frequency domain information of the image; (3) retaining a phase spectrum and obtaining the high-frequency information of a magnitude spectrum by using a Butterworth high-pass filter; (4) carrying out quaternion Fourier inversion on the phase spectrum and the high-frequency information of the magnitude spectrum to obtain a characteristic pattern; and (5) filtering the characteristic pattern by using a gaussian pyramid and reducing dimensions to obtain a saliency map; (6) carrying out threshold segmentation and binaryzation on the saliency map to obtain a region-of-interest template; and (7) raising the dimensions of the template and carrying out masking operation on an original image so as to obtain the final region-of-interest. The method realizes the rapid and accurate location of the region-of-interest, has the advantages of high accuracy of region description, low computation complexity and the like and can be applied in fields such as environmental monitoring, land utilization, agricultural investigation and the like.

The invention belongs to the technical field of remote sensing image processing, in particular to a hyperspectral remote sensing image mixed pixel decomposition method based on independent component analysis. According to the physical characteristics of hyperspectral images, the invention introduces abundance sum-to-one constraint and abundance nonnegative constraint into the target function of independent component analysis, and provides a self-adapting abundance modeling method to describe probability distribution of data, thus presenting favorable applicability towards different remote data. The method can effectively solve the problems of high mixing degree and remote sensing data mixed pixel decomposition under various interferences, and has significant application value in high-precision ground feature classification and detection and recognition of ground targets based on multispectral and hyperspectral remote images.

The present invention provides a novel convolutional neural network-based remote-sensing image road extraction method, and belongs to the field of remote-sensing image treatment. The method comprises the steps of establishing a full convolution neural network, obtaining a penalty weight and constructing a new loss function based on a minimum euclidean distance between a pixel point and a road region, training a full convolution neural network model through the loss function obtained based on the road structure, and extracting a road based on the well trained model. According to the technical scheme of the invention, the geometric structure of a road is embodied in the loss function in the form of the penalty weight. Therefore, not only the global structural characteristics of the road are reserved, but also a corresponding significant coefficient is provided for each pixel point. Based on the method of the invention, an obtained remote-sensing image road detection model is improved in both recall ratio and precision compared with the prior art, so that a complete road structure can be obtained. The road interruption condition is improved and the roof false detection condition is excluded. Therefore, a better road extraction effect is obtained.

The present invention discloses a remote sensing image region-of-interest detection method based on multi-significant-feature fusion, belonging to the technical fields of remote sensing image processing and image identification. The remote sensing image region-of-interest detection method comprises the following steps: 1) obtaining color channels of one group of input remote sensing images and calculating a color histogram of each color channel; 2) calculating a standard significant weight of each color channel according to the color histograms; 3) calculating an information content significant feature image; 4) converting one group of input remote sensing images from an RGB color space to a CIE Lab color space; 5) utilizing a clustering algorithm to obtain clusters; 6) calculating a significant value of each cluster, and obtaining a common significant feature image; 7) fusing the information content significant feature image with the common significant feature image to obtain a final significant image; and 8) performing threshold segmentation through an OTSU method to extract a region of interest. Compared with a traditional method, the remote sensing image region-of-interest detection method of the present invention achieves accurate detection for a remote sensing image region-of-interest on the premise of not having a prior knowledge base, thus the remote sensing image region-of-interest detection method can be widely applied to fields such as environment monitoring, land utilization and agricultural investigation.

The invention discloses a high-resolution remote sensing image crop classification method based on deep learning, and belongs to the technical field of satellite remote sensing image processing and application. The objective of the invention is to solve the problems of fuzzy ground object boundary, high algorithm complexity, neglected characteristic waveband information and the like in crop classification of an existing high-resolution remote sensing image, and the problem that crop classification cannot be carried out on the high-resolution remote sensing image by using an existing neural network method. According to the method, a 36-waveband combination mode is adopted, a VGG neural network result is designed, deep learning of the VGG neural network is utilized, and accurate classification of crop plots in a high-resolution remote sensing image with the resolution of 10 m is achieved through multiple iterations.

The invention belongs to the technical field of remote sensing image processing and particularly relates to a dimension reducing and sorting method of a hyperspectral imagery based on blocking low rank tensor analysis. According to the dimension reducing and sorting method of the hyperspectral imagery based on the blocking low rank tensor analysis, a blocking idea is introduced into a dimension reducing method of the hyperspectral imagery based on the low rank tensor analysis according to three-dimensional data structures, spectral characteristics and correlated characteristics of local spaces of the hyperspectral imagery, the problems that integral spatial correlation of the imagery is weaker and negative effects of configuration of dimension-reduced subspace dimension on a dimension reducing effect are overcome, finally a novel dimension reducing method capable of greatly improving total sorting precision of the imagery is realized, and the novel dimension reducing method is a blocking low rank tensor analysis method. An algorithm presents well applicability on various different hyperspectral data (including simulation data and actual data sets). The dimension reducing and sorting method of the hyperspectral imagery based on the blocking low rank tensor analysis has important application values in high-precision terrain classification aspects based on the hyperspectral imagery.

The invention belongs to the technical field of remote-sensing image processing and application and relates to a method for detecting a million-ton large-scale ship target in a high-resolution multispectral remote sensing image. The method comprises the following steps: firstly, gradient-based image segmentation is carried out on an image; secondly, geometrical characteristic and color characteristic of the segmentation object are extracted; and thirdly, by the utilization of a large-scale ship characteristic prior knowledge base, the segmentation object is classified by a fuzzy rule so as to obtain a large-scale ship object. In addition, as for detection of a large-scale ship in the port area, sea and land segmentation is carried out by the utilization of sea and land boundary information in a port prior knowledge base so as to remove the influence of the land part at a port. By adding a postprocessing step, error results detected at a non-million-ton berth are eliminated by the utilization of berth information in the port prior knowledge base. By full utilization of rich spectral information and high resolution of the high-resolution multispectral remote sensing image and by the use of the prior knowledge base, high reliability of detection results is guaranteed, and manual intervention is little during the detection process.

An optical remote sensing image processing performance evaluating method belongs to the optical remote sensing image processing and evaluating technical field; the method comprises the following steps: building an image objective evaluate index system for image gray scale, texture and edge information, and evaluating the processing performance of a processing algorithm on detail maintenance and sharpness enhancement; combining with a modulation transfer function (MTF) estimate method based on an edge image and a natural image, and using the MTF curve integration area to evaluate the whole lifting performance of the processing algorithm on each frequency band MTF; proposing an image signal to noise ratio (SNR) evaluate index and method, and evaluating the integral processing performance of the processing algorithm on detail maintenance enhancement and noise inhibition; providing a ringing and aliasing effect evaluate method, and evaluating the processing performance of the processing algorithm on pseudo image inhibition. The advantages are that the method is suitable for comprehensively and objectively evaluating the image restoration algorithmic performance advantages and disadvantages, thus guiding processing algorithm parameter optimization and model improvement.

The invention discloses a method for extracting a remote sensing image interesting area based on multi-scale feature fusion, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: (1) adopting a saliency analysis method based on a multi-scale spectrum residual error to generate a multi-scale low frequency saliency image of the remote sensing image, (2) obtaining multi-scale high frequency saliency images in the horizontal direction, the perpendicular direction and the diagonal direction through integer wavelet transformation, (3) obtaining a luminance saliency image and a direction saliency image through cross-scale weighting and cross-scale fusion, (4) combining the luminance saliency image and the direction saliency image to generate a main saliency image, and (5) carrying out threshold segmentation to extract the interesting area through the OTSU. Compared with a traditional method, the method has the advantages of being low in computing complexity and effectively improving area extraction efficiency and area extraction accuracy. Due to the fact that spectrums and color information of the remote sensing image are not required to be obtained, the method can be directly used for interesting area extraction of a high-resolution full-color remote sensing image. The method has good application value in the fields of land planning, environment monitoring, forestry investigation and the like.