382 results about "Morphological processing" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project crack detection method and system based on intelligent visual identification, and the method comprises the following steps: obtaining a crack image, enhancing the brightness and contrast in different regions, extracting and optimizing the crack edge, executing morphological processing, denoising and smoothing, segmenting a crack region, and judging the connection condition. And analyzing the geometrical characteristic detection connectivity, and calibrating the crack position to obtain a positioning result. According to the method, through dynamic adjustment of image brightness and contrast, the crack is more prominent in a complex background, especially under non-uniform illumination, crack identification difficulty caused by illumination inconsistency is reduced, crack edges are optimized through morphological operation, the missing part of the crack can be filled, the crack contour can be smoothed, and noise can be eliminated. And in the crack segmentation stage, through self-adaptive threshold and geometric feature analysis, the fracture and connection conditions of the crack are accurately judged, and positioning of the crack end point and the continuous area of the crack end point is achieved.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention relates to the technical field of electromagnetic spectrum monitoring, and particularly discloses a distance-based electromagnetic spectrum monitoring abnormal data detection method, which comprises the following steps of: performing short-time Fourier transform and normalization processing on an acquired original signal to generate an energy density distribution characteristic graph; constructing a multivariate Gaussian distribution model based on non-abnormal historical data; during real-time monitoring, the mahalanobis distance between the collected data and the mean vector of the historical model is calculated after the collected data is preprocessed. And comparing the distance metric value with a preset threshold value to preliminarily judge abnormity, and calculating a distance fluctuation variance through a sliding window mechanism to perform secondary verification. And finally, processing and positioning anomalies by using image morphology, and dividing the degree of anomalies according to the relative deviation between the energy density and the mean value of the historical model. According to the method, the mahalanobis distance and the multivariate Gaussian distribution are introduced, secondary verification and abnormal positioning are combined, the limitation of a traditional method is overcome, the detection accuracy and reliability are effectively improved, the misjudgment and missing judgment rate is reduced, and the method does not depend on a large amount of labeled data and is high in practicability.

The invention provides a mountainous area tunnel crack intelligent identification and detection method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting multispectral image data, and carrying out the adaptive preprocessing to obtain an enhanced feature map; a double-flow network is combined with a space-channel cascade attention module to extract fusion features; constructing multi-scale feature representation through a feature association graph network; training the network by adopting a joint optimization target; and morphological processing and connectivity analysis are carried out to realize accurate identification and classification of the crack. According to the invention, the detection precision and the anti-interference capability of the tunnel crack in the complex environment are improved.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention discloses a power equipment defect intelligent identification method, system and device and a medium, and the method comprises the steps: collecting an original image of power equipment, and screening the original image of the power equipment to obtain a channel image; carrying out enhancement processing on the channel image and then calculating a gray scale difference value to obtain a gray scale image; converting the gray level image into a frequency spectrum image by adopting fast Fourier transform, and constructing a Gaussian filtering function to carry out convolution and inverse transformation on the frequency spectrum image to obtain a spatial domain image; and performing adaptive threshold segmentation on the spatial domain image, dividing the image into a defect area and a non-defect area, obtaining a segmented image, and performing morphological processing on the segmented image to obtain an electrical equipment defect identification result. According to the method, the problem of aliasing in traditional spatial domain processing is solved, the defect area is accurately extracted, short-time interference and real defects can be effectively distinguished, and the segmentation accuracy is improved.

The invention discloses a method for identifying small target features in a ray detection image, and relates to the field of image processing, and the method comprises the steps: carrying out the size unification, pixel standardization and normalization preprocessing of an original ray image; constructing a training sample set through image region cutting, and introducing a dynamic sampling strategy to realize positive and negative sample proportion adaptive control; enhancing the number and diversity of small target samples in a training set by using point-shaped and linear artificial defect generation strategies; constructing an image segmentation model and introducing feature jump connection to fuse shallow space and deep semantic information; combining Dice loss and Focal loss to form a composite loss function, and guiding the model to pay attention to a target region with a small area and weak gray level; and finally, a segmentation result is optimized through morphological processing and connected domain analysis, and structured target detection information is output. According to the invention, the recognition accuracy and integrity of the tiny target in the ray image can be effectively improved, and the adaptive capacity of the detection method to the change of the imaging quality is enhanced.

The invention discloses a drilling rock core image automatic identification and recording method and system based on deep learning. The method comprises the following steps: preprocessing a collected to-be-detected drilling rock core image; the preprocessed image is input into a trained Transform semantic segmentation model, a pixel-level probability graph is output, and a binary mask image is generated through binarization processing; performing morphological processing, connected domain analysis and contour detection on the binary mask image to obtain edge coordinate information of the rock core frame, extracting a standardized rock core image from the original image according to the edge coordinate information, and performing result storage and visualization; inputting the standardized rock core image into a trained Transform multi-category rock core structure recognition model, outputting a pixel-level probability graph of multiple channels, and generating a binary mask image of each channel through binarization processing; and three-rate calculation and multi-structure statistical analysis of the drilled rock core are carried out, and data output and visualization are carried out.

The invention provides an exosome automatic identification and analysis method based on microscopic imaging, and belongs to the technical field of exosome identification and analysis, and the method comprises the steps: collecting an exosome fluorescence microscopic image, carrying out the preprocessing, and obtaining an exosome region through adaptive threshold segmentation and morphological processing. A GPU is adopted to process and extract morphological, texture and statistical feature matrixes in parallel, and meanwhile, the CPU recognizes an abnormal region. And combining the features to form a region feature set, inputting the region feature set into the preliminary screening model for scoring and screening high-credibility regions. Deep feature extraction is carried out on the high-credibility region, and multiple features are fused by using an attention fusion mechanism to construct vectors; and inputting a depth classifier to obtain a classification probability matrix and a screening result. And finally outputting the number, position, classification and credibility score of the exosomes. The problems that an existing exosome analysis method often depends on manual recognition, and automatic and high-throughput analysis is difficult to achieve are solved.

The invention proposes a dynamic target detection method based on deep learning, and the method comprises the steps: extracting a dynamic target through unsupervised background modeling and morphological processing, combining a deep feature network with a lightweight visibility network, and outputting a target structured state; constructing a trajectory velocity modeling network through a differential equation according to the bounding box and the semantic features, generating a target motion trajectory, and performing numerical integration on the differential equation to obtain a predicted spatial position; fusing the semantic feature vector with the predicted spatial position to generate future appearance features of the target; when a frame image is input, constructing a candidate region based on the predicted spatial position, and calculating a matching confidence coefficient between the candidate region and a future appearance feature of the target to confirm a target position and a target feature; and if the matching confidence coefficient is lower than a preset threshold value, the previous frame state is left, and updating is not carried out, so that the robustness in a shielding or fuzzy state is improved.

The invention discloses a welding splash segmentation and recognition system and method based on deep learning, and the system comprises the steps: setting an image collection module and an image preprocessing module, constructing a splash segmentation module through an improved deep learning network, carrying out the pixel-level segmentation of a processed image, recognizing a welding splash region, and outputting an initial segmentation result. Morphological processing and boundary optimization are carried out on the initial segmentation result, a segmentation result is generated, the segmentation result is converted into position information under an actual coordinate system, guidance is provided for automatic grinding equipment, a detection result is displayed on an original image in an overlapping mode, and a visual detection result is provided. The detection precision of the welding spatter of the body in white is remarkably improved, and the method has higher adaptability to the welding spatter in a scattering shape and an irregular shape; detection tasks under complex background and variable illumination conditions can be processed; the real-time performance is good, and the production line rhythm requirement is met; accurate area positioning information is provided for automatic grinding equipment, and the grinding efficiency and quality are improved.

The invention relates to the field of image detection, in particular to a road pit detection method and system based on vision and 4D millimeter wave radar. Obtaining a current road image, inputting the current road image into a pre-constructed three-dimensional pit hole model, and outputting a pit hole detection result of the current road image; the construction process of the three-dimensional pit model comprises the following steps: obtaining a road image, inputting the road image into a segmentation network, and generating a pit image; performing morphological processing on the pit image to obtain geometric feature parameters; and obtaining pit boundary coordinates based on the geometric feature parameters, mapping the pit boundary coordinates to a radar coordinate system in the 4D millimeter wave radar, and generating a three-dimensional pit model. Visual perception and 4D millimeter wave radar data are fused, the pit hole detection precision is improved, the limitation of a single sensor under complex road conditions is overcome, pit hole depth and form restoration is achieved through three-dimensional modeling, and the vehicle driving safety and the obstacle avoidance capacity are further improved.

The invention belongs to the technical field of medical image processing, and discloses a medical image diagnosis system based on image and text feature fusion, and the system comprises an image processing module which is responsible for carrying out the enhancement, segmentation and feature extraction of a medical image; the text analysis module is responsible for extracting key clinical information from the electronic medical record; the semantic alignment and feature fusion module is used for embedding image features and text features into a unified vector space to realize fusion of multi-modal data; the model training and optimizing module is responsible for training and optimizing a medical diagnosis system model; and the clinical application module is responsible for applying the trained medical diagnosis system model to an actual medical environment to assist doctors in disease diagnosis. According to the medical image diagnosis system based on image and text feature fusion, by combining image enhancement, morphological processing, NLP analysis and multi-modal feature fusion, the illness state of a patient can be understood more comprehensively, the diagnosis accuracy is improved, and a more reliable auxiliary decision making basis is provided for doctors.

The invention relates to the technical field of data processing, in particular to an AR-based live broadcast real-time interaction system and method, and the system comprises a motion capture device, an environment perception device, a dual-host processing module, a virtual-real fusion module, an interaction response module, a data distribution module and an AR display terminal. And the double hosts parse the action data and the environment data in parallel and simulate a physical feedback track of a virtual element in combination with a physical engine, so that low-delay response of an interaction instruction is realized. And the virtual-real fusion module adopts a timestamp synchronization mechanism to align the rendered picture and the anchor picture after green screen matting, and eliminates edge sawteeth through image morphological processing to generate high-precision fusion streaming media data. And the AR display terminal projects the virtual elements to real scene space coordinates by adopting an optical superposition technology, so that an anchor previews a virtual-real fusion effect in real time. According to the invention, the dynamic interaction capability and shadow consistency of the virtual elements and the real scene are improved, and the immersive experience of the live scene is optimized.

The invention discloses a two-stage cascaded distributed optical fiber sound wave sensing vehicle trajectory reconstruction method and system, belongs to the technical field of intelligent traffic perception, and aims to solve the problem that geometric accuracy and topological integrity are difficult to consider under the conditions of low signal-to-noise ratio and complex road conditions in the conventional DAS vehicle trajectory reconstruction technology. The method comprises the following steps: in the first stage, carrying out nonlinear enhancement on original vibration data and converting the original vibration data into a two-dimensional space-time grayscale image, inputting the two-dimensional space-time grayscale image into a deep learning semantic segmentation network to generate a high-fidelity track mask, extracting a skeleton through morphological processing, and constructing an initial candidate topological graph; and in the second stage, multi-hop neighborhood kinematics constraint pruning is performed on the initial topological graph, false connection edges are eliminated, global optimization matching is performed by using a coupling cost function to repair fractures, then isolated points are recalled through local geometric scores, and finally a vehicle trajectory graph with complete topology is output. According to the method, geometric accuracy and topological integrity can be effectively considered in complex scenes such as speed change, lane change and multi-vehicle intersection, and the robustness of all-weather traffic flow monitoring is improved.

The invention relates to a method for processing medical images of human tissue of an area of a patient's body and in particular of the breast by means of a medical imaging device (1) comprising a microwave probe array consisting of K>1 probes spaced apart from one another, the array comprising P>1 different configurations defining transmitting probes and receiving probes for one or more position(s) around the area, in which the transmitting probes are configured to transmit microwave signals so as to illuminate an area of the body and the receiving probes are configured to receive microwave signals after scattering and reflection in the area, the probes being capable, in a complementary manner, of being configured to transmit and receive simultaneously.

The invention discloses a reinforcement corrosion degree detection method based on a quantum image processing algorithm, and the method comprises the steps: collecting a reinforcement corrosion image, and carrying out the graying and median filtering noise reduction processing of the image; constructing a quantum image model, and performing morphological processing on the image; carrying out Otsu threshold segmentation on the image based on a PES algorithm; integrating a gray histogram and three-level wavelet decomposition energy for the image, constructing a multi-dimensional feature set, carrying out standardized preprocessing and focusing three-level decomposition, abandoning redundant status data, and carrying out feature extraction on the quantum image based on a positive index relationship between image information entropy and thickness loss; and repeating the above steps to obtain a reinforcement corrosion degree prediction data set, and training the convolutional neural network through the reinforcement corrosion degree prediction data set to obtain the convolutional neural network for diagnosing the reinforcement corrosion type. The image processing effect is improved, the feature extraction efficiency and precision are improved, and the classification efficiency is improved.

The invention belongs to the technical field of image processing, and particularly relates to an MBR flat membrane surface physical damage visual identification method and system, and the method comprises the steps: carrying out the low-pass filtering of a microscopic gray image, so as to melt a micropore background; calculating a texture variation index based on the outlier degree of the gray values of the neighborhood pixel points and the neighborhood gray dynamic contrast gain; determining a damage aggregation weight by using a space attenuation accumulation value, and eliminating isolated noise points; the texture variation index and the damage aggregation weight are fused through a self-adaptive gating function, and damage confidence is generated; and finally marking a connected damage region based on statistical threshold binarization and morphological processing. According to the method, dense micropore interference can be effectively inhibited, and the physical damage identification precision is improved.

The invention belongs to the technical field of three-dimensional measurement, and discloses a power transmission line icing volume measurement method, system and device, a medium and a program product, and the method comprises the steps: employing a binocular camera to shoot and correct an icing power transmission line view, and obtaining internal and external parameters, performing depth estimation on the power transmission line view based on the corrected power transmission line view and the internal and external parameters, training a conductor detection model and inputting the corrected power transmission line view to obtain a detection frame and a corresponding confidence coefficient, using the detection frame with the highest confidence coefficient as a prompt, inputting the prompt and the corrected power transmission line view into an SAM2 network for segmentation, and obtaining a segmentation result; de-noising and morphological processing are carried out on the masks of the ice-coated wires, and finally the ice-coated area of the power transmission line is calculated. The system, the equipment and the medium are used for implementing the method. The program product comprises a computer program for implementing the method; the method for measuring the icing volume of the power transmission line has the effects of stability, reliability, high efficiency, accuracy and high safety.

The invention discloses a mask target detection method based on an improved YOLOWorld world model, and the method comprises the following steps: obtaining a to-be-detected mask image, carrying out the data preprocessing of the obtained to-be-detected mask image, so as to obtain a mask image after the data preprocessing, carrying out the image processing of the mask image after the data preprocessing, and obtaining a mask target. And performing image processing on the mask target detection model to obtain a mask image after image processing, and inputting the obtained mask image after image processing into a pre-trained mask target detection model to obtain a final detection result. The technical problem that the false detection problem of an existing color space conversion method under a complex background is prominent can be solved, the technical problem that an existing morphological processing method is difficult to achieve balance between real-time performance and precision can be solved, and the technical problem that an existing template matching method is limited in model recognition capacity can be solved.

The invention provides an agricultural image background removal method, and relates to the technical field of agricultural image processing, and the method comprises the steps: carrying out the adaptive illumination condition judgment of a preprocessed agricultural image; calculating and selecting corresponding vegetation indexes according to the light condition judgment result, and calculating to generate a plurality of vegetation index feature maps; inputting the plurality of vegetation index feature maps into a machine learning model, learning the relative importance of each vegetation index through the machine learning model, and generating a comprehensive vegetation feature map; performing vegetation texture region judgment on the preprocessed agricultural image to obtain a target vegetation texture region judgment result; judging a foreground mask based on the comprehensive vegetation feature map and a target vegetation texture region judgment result; performing morphological processing on the foreground mask to obtain an optimized foreground mask; and according to the optimized foreground mask, removing a background from the agricultural image in the original RGB format to obtain a target vegetation image.

The invention discloses a defect classification and detection method based on sub-pixel template matching, and the method comprises the following steps: eliminating a vignetting effect through employing a frequency domain filtering and dynamic compensation technology, and constructing a homogenized image substrate; sub-pixel-level positioning is realized through a shape feature model based on an embedded template; intelligent completion of missing sub-pixels is realized through row and column spacing analysis and an equipartition interpolation algorithm; through morphological processing of a template and a defect region, mapping of sub-pixel points and region intersection operation, the overlap ratio of the two regions is calculated, and then whether the defect is a sub-pixel defect is judged.

The invention relates to the technical field of image processing, in particular to an aluminum alloy die casting surface defect detection method based on machine vision. The method comprises the steps of obtaining a grayscale image of the surface of a die casting; constructing a Gaussian scale space of the grayscale image, and obtaining scale images under a plurality of scales; calculating a defect saliency index corresponding to each pixel point in the grayscale image based on the multi-scale image, and generating a defect saliency map; and performing threshold segmentation on the defect saliency map to obtain a segmented image, performing morphological processing identification on the segmented image, and determining the position of a surface defect area. According to the invention, missed detection and false detection in the surface detection process of the aluminum alloy die casting can be reduced.

The invention discloses a detection method for fertilizer production, and relates to the technical field of fertilizer detection.The detection method comprises the following steps that a near-infrared multispectral visual detection system is installed at the working section before packaging of a fertilizer production line, the positions of a camera and an illumination source are adjusted, and multispectral images of fertilizer particles are collected; sequentially carrying out dark current correction and flat field correction on the acquired image, eliminating noise and uneven illumination, and then carrying out median filtering to remove random noise; selecting a waveband image with the best contrast ratio, separating particles from the background through threshold segmentation, and positioning each independent particle after optimizing the boundary through morphological processing; and based on the multi-band reflectivity difference, analyzing the surface material of the particles pixel by pixel, and counting the proportion of the coating area of each particle to obtain the coating coverage rate of the single particle. By introducing the near-infrared multispectral visual detection technology and the coating uniformity quantitative analysis model, non-destructive and high-precision online detection of the coating coverage rate of the fertilizer can be realized.

The invention provides a maglev train bottom bolt looseness identification method based on deep learning. The method comprises the following steps: acquiring a bottom image of a maglev train by adopting imaging equipment, and carrying out bolt target detection on an image in a bottom image data set through an improved YOLOv8 network to obtain a bolt image; performing classification processing on the bolt image through a lightweight network RepViT fused with a lightweight ViT design principle; color space conversion, image binaryzation, morphological processing and connected domain screening processing are carried out on the bolt image to extract an anti-loosening line, and whether the vehicle bottom bolt in the bolt image is loosened or not is judged based on the bolt anti-loosening line according to loosening characteristics of different types of bolts under multiple visual angles. According to the method, the detection precision is improved by means of improving a target detection network structure, refining bolt loosening characteristics, providing adaptive detection methods for different types of bolts and the like; by designing a lightweight classification network mode, the calculation amount and time consumption are reduced, and the detection efficiency is improved.

The invention relates to a concrete structure crack detection method under a complex background based on transfer learning and a multi-stage image processing technology, and the method employs a combined process of image filtering, threshold segmentation, morphological processing and the like to extract cracks, guarantees the precise division of crack boundaries, and facilitates the accurate measurement. In quantitative analysis, the length and the width of the crack are measured in combination with geometric dimensions and physical transformation, and important data are provided for concrete crack evaluation. And finally, strictly verifying the proposed method by using a self-established data set. Experimental results show that the crack detection accuracy of TLGoogleNet is as high as 99.47%, and the error range of the length and width of the crack is always kept within about 5%. The results fully manifest high precision and strong robustness of the developed method.

The invention discloses a surrounding rock fracture three-dimensional reconstruction method and system based on three-dimensional laser scanning. The reconstruction method comprises the following steps: acquiring roadway surrounding rock three-dimensional point cloud data through three-dimensional laser scanning; preprocessing the three-dimensional point cloud data, and removing point cloud data, irrelevant to surrounding rocks, of equipment, pedestrians and the like; through a density-based point cloud clustering algorithm, obtaining point cloud blocks with relatively low density, and marking the point cloud blocks as crack point clouds; obtaining a fracture edge shape by using closed operation in a morphological processing algorithm; the maximum depth, the average depth and the depth distribution of the fracture are obtained by fitting a roadway plane, obtaining a reference surface and calculating the clustering of points in the fracture to the reference surface, so that the three-dimensional reconstruction of the fracture is realized. The method can make up for the defect that existing fracture recognition only stays at two-dimensional reconstruction and cannot acquire fracture depth information, and improves understanding of fracture conditions.

The invention discloses a placenta implantation analysis method based on an ultrasonic image, particularly relates to the technical field of medical image analysis, and is used for solving the problem that the placenta implantation condition is difficult to accurately diagnose due to artifact interference caused by complex reflection of an existing placenta area and adjacent blood vessels. The method comprises the following steps: acquiring ultrasonic radio frequency data, generating a two-dimensional gray scale image and a Doppler blood flow image, constructing a dynamic interference distribution diagram, extracting artifact time sequence characteristics, realizing frequency domain separation of an artifact region and generation of a multi-reflection interference pixel set in combination with wavelet transform, and identifying a real blood flow signal through an adaptive threshold algorithm. According to the method, artifacts and real signals are fused to generate an artifact-removed image, morphological processing based on structural elements is carried out on the artifact-removed image, continuous boundary information of the placenta and the uterine wall is extracted, suspicious blood vessel invasion paths are marked in a partitioned mode, and the accuracy of image analysis is improved.

The invention provides an urban water body efficient extraction method for a high-resolution satellite image, and belongs to the technical field of image processing, and the method comprises the following steps: 1, preprocessing satellite image data; 2, water body extraction based on index characteristics; step 3, water body extraction based on spectral characteristics; 4, water body extraction based on numerical characteristics; and 5, carrying out morphological processing on the water body binary image based on spatial characteristics. According to the method, when water body extraction is carried out on a high-resolution image with only four wave bands, compared with the method which only depends on calculation of a spectral index and setting of a threshold value, spectral characteristics of a real water body are comprehensively considered, and threshold values such as a spectral slope and a wave band value are set, so that the water body identification precision can be effectively improved; and the spatial distribution characteristics of the water body are further combined and morphological operation is carried out, so that the error of water body extraction caused by pixels such as building shadows in the urban complex environment can be greatly weakened.