346results about How to "Improve segmentation" patented technology

The invention provides an auricle anaphora segmentation method and system, and the method comprises the steps: inputting a to-be-segmented human ear image and text description into an auricle anaphora segmentation model which comprises a text coding module, a text-guided visual coding module and visual decoding module, and an angle transformation module; the text coding module obtains a text feature # imgabs0 #; a visual coding module realizes fusion of text features and image features through a structure organized into four stages, a visual encoder in each stage generates visual features # imgabs1 #, a cross-modal sensing module aligns # imgabs2 # and # imgabs3 # to obtain multi-modal features # imgabs4 # and # imgabs5 #, each element in the multi-modal features # imgabs4 # and # imgabs5 # is weighted by an attention gating module to obtain weighted multi-modal features # imgabs6 #, and the weighted multi-modal features # imgabs6 # and # imgabs5 # are weighted by an attention gating module. Adding the elements and # imgabs7 # to generate an enhanced visual feature # imgabs8 #, inputting # imgabs9 # into a visual decoding module to gradually recover the spatial resolution of the image, and further fusing the text and the visual feature to output a multi-scale feature; the angle transformation module carries out angle transformation on the multi-scale features and outputs a segmentation mask of a text description related region. According to the invention, the auricle can be subjected to anaphora segmentation.

The invention discloses a semi-supervised medical image segmentation method based on eye movement guide mixed data enhancement. The method comprises the following steps: collecting fixation position information of a doctor when reading a medical image through eye movement tracking equipment; using a teacher-student network model and adopting a two-stage training mode to train the model; the method comprises the following steps: taking GazeMix as a data enhancement strategy in training, extracting a fixation area in a labeled or unlabeled image, and pasting the fixation area to the background of other unlabeled images to generate a mixed image data pair; in the first stage, a GazeMix data enhancement strategy is combined to pre-train a teacher network; in the second stage, a student network is trained, GazeMix generates diversified data, and meanwhile, a teacher network is pre-trained for pseudo tag generation; multi-scale gaze perception is adopted, and model perception information is extracted on different scales and used for perception loss calculation; and the optimizer iteratively optimizes the student network parameters, and updates the teacher network by using an index moving average method.

The invention discloses a lightweight semantic segmentation network and method for power grid inspection, and the method comprises the steps: employing an encoder-decoder architecture, inputting original data into a lightweight MobileNet v2 backbone network for feature extraction at an encoder part, inputting an obtained feature map into a lightweight large-scale ASPP module, and carrying out the feature extraction; further performing multi-scale feature extraction on the high-dimensional feature map; and in the decoder part, high-dimensional features and low-dimensional features obtained by the backbone network are respectively input into a cross attention wavelet fusion module, after an obtained feature map is fused with the output of a decoder, the feature map is recovered to the size of original data through down-sampling operation by a full-dimensional dynamic convolution post-processing module, and a final prediction mask is obtained. According to the invention, by optimizing a coding and decoding structure, the receptive field of the model and the modeling capability of a narrow and long target are improved, the balance between the precision and the complexity can be effectively realized, and the method is suitable for an unmanned aerial vehicle inspection system with limited resources.

The invention relates to the technical field of image processing, and discloses a scoliosis angle intelligent measurement method and system based on spine CT three-dimensional reconstruction. The method comprises the following steps: carrying out adaptive threshold segmentation and gray optimization processing on a spine CT sequence image to obtain a standardized preprocessed image, and carrying out adaptive bone tissue region identification and boundary enhancement processing to obtain spine skeleton segmentation data; performing three-dimensional grid construction and curved surface smoothing processing on the spine skeleton segmentation data to obtain spine three-dimensional model data; carrying out centrum gap identification and key point positioning processing on the spine three-dimensional model data to obtain a centrum feature data set, and carrying out spine midline curve construction and end centrum marking processing to obtain Cobb angle data; and performing three-dimensional space reconstruction and multi-plane projection processing on the Cobb angle data to obtain a measurement result report. According to the method and the device, full-automatic processing from acquisition of the CT image to generation of the final measurement result report is realized on the premise of ensuring the measurement accuracy.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

The invention discloses a multi-scale feature fusion triple branch network method for multi-organ segmentation, and the method comprises the following steps: extracting local features through a CNN, and extracting global features through Transform; the features extracted by the CNN and the Transfomer are processed by using space attention and channel attention respectively, the processed features are fused, and output features are obtained through a feature processing module; the method comprises the following steps: setting a mask mechanism Smart Mask, improving a fixed mask mode in a Swin Transform, and capturing a long-distance dependency relationship; according to the method, global features in a medical image are learned through parallel Vision Mama, features obtained after CNN and Transform fusion are guided, and a segmented image is obtained. The CNN, Transform and Mamba encoders are combined, so that efficient medical image segmentation is realized. And the introduced parallel Vision Mama layer increases a small number of parameters and enhances the global context capture capability at the same time, so that the performance and efficiency of the model are remarkably improved.

The invention belongs to the technical field of image recognition, and discloses a few-sample defect detection method based on a prototype prompt fine-tuning visual basic model SAM. Prototype prior information about an image to be segmented is extracted from a small number of example image-mask pairs, so that prior guidance of human beings is replaced; for defect data, a representative foreground prompt point, a high-confidence background prompt point (sparse prompt) and a foreground Mask (dense prompt) are extracted from prototype prior to serve as prototype prompt, and a visual basic model SAM is guided to achieve more accurate segmentation. A visual basic model SAM is finely adjusted by using prototype prompt and a small amount of defect data, a prompt encoder and an image encoder of the SAM are frozen, an LoRA bypass is injected into the image encoder, and the SAM is enabled to better adapt to defect data distribution by updating parameters of the LoRA bypass and a lightweight mask decoder.

The invention discloses a medical image segmentation method and system based on basic model assisted semi-supervised learning. In a medical image segmentation task, by introducing an innovative method of combining a visual basic model (SAM) and a U-Net model, the labeling precision and the segmentation effect are remarkably improved. Compared with an existing semi-supervised learning method, the method has the advantages that the problem of low-quality false labels is solved by introducing a reliability scoring mechanism and a foreground conflict processing strategy, and unreliable images are effectively utilized through a region exchange strategy. Finally, the invention provides a new solution for solving the problems of low false label quality and high data annotation cost in medical image segmentation, greatly improves the performance of medical image segmentation, and shows an excellent segmentation effect on a plurality of reference data sets.

The invention discloses a remote sensing image semantic segmentation-oriented spatial spectrum context feature enhancement method and device. The method comprises the following steps of: constructing a neural network model based on an encoder-decoder architecture; inputting the preprocessed remote sensing image set into a feature coding block, and extracting hierarchical features; according to the model, spectrum attention and position self-attention mechanisms are integrated through a collaborative attention module, and the sensitivity of network spectrum and spatial context information is remarkably enhanced; the network coding module efficiently compresses and codes the features before entering a decoding stage; a cross fusion module is applied to intelligently fuse features between a coding block and a decoding block to ensure that information is not lost in a decoding process; the feature map is mapped into a pixel-level category label through a Softmax layer, precise semantic segmentation is achieved, and a high-quality classification result is obtained. According to the method, through collaborative modeling of the spectrum and the spatial context, high-frequency details are effectively reserved, and the precision of semantic segmentation is improved.

The invention discloses an edge-based open vocabulary segmentation model target identification method. The method comprises the steps of obtaining a target image; initializing Gaussian white noise as a noise contour mask pattern of a target; inputting the target image and the noise contour mask image into an open vocabulary segmentation model to execute the following operations: extracting multi-scale features of the target from the target image and the noise contour mask image; encoding the noise contour mask pattern to obtain encoding features; the multi-scale features are used as condition features to be fused with the coding features, and fusion features are obtained; predicting a de-noised contour mask graph of the target according to the fusion features; and according to the de-noised contour mask graph and the multi-scale features, a single full connection layer is used to predict classification and positioning of the target. According to the method, target identification based on the open vocabulary segmentation model can be effectively and quickly completed.

The invention discloses a multi-mode unmanned aerial vehicle image segmentation method. The method comprises the steps of 1, constructing an unmanned aerial vehicle image semantic segmentation base network; 2, capturing high-resolution local features in a shallow layer through a convolutional neural network (CNN) module, extracting from deep features of the image through a multi-head self-attention mechanism in a transformer module, and fusing the local features with deep global features through jump connection; 3, constructing a parallel convolution module, and optimizing the features fused in the step 2; 4, combining feature extraction results in the step 2 and the step 3, fusing feature information through a cross attention mechanism, and improving a segmentation effect in a complex scene; and 5, constructing a double attention module, and optimizing local and global information processing of the image. The method has the characteristics that the global and local features are efficiently fused, the multi-scale target is accurately segmented, and the robustness in a complex scene is remarkably improved.

The invention discloses a polyp image segmentation method based on boundary clue depth fusion, and the method comprises the steps: inputting an intestinal polyp image into a backbone network through constructing a boundary clue depth fusion network, and extracting different levels of feature information, so as to guide the extraction of a polyp region; a boundary clue mining module is constructed to obtain boundary detail information by means of partial hierarchical features, and more accurate positioning of a polyp area is realized; constructing a foreground target enhancement module to enhance the learning of the network on the overall structure characteristics of the foreground target in combination with the reverse attention branch, so that the network focuses on the foreground target; and constructing a depth feature fusion module to integrate boundary information and foreground information, further improving the segmentation performance through cascade feature fusion and boundary information guidance, and finally obtaining a polyp segmentation result by using the trained model. According to the method, the limitation of the polyp in boundary uncertainty and morphological diversity is overcome, the weighted joint scale difference loss optimization model is adopted in training, and high-precision recognition and segmentation of the polyp region are realized.

The invention discloses a mandibular neural tube CBCT panoramic image segmentation network training method based on a Mama architecture, and relates to an image processing technology in the field of computer vision. According to the segmentation network training method, an encoder and decoder architecture is adopted, tubular structure features are extracted by enlarging a receptive field and learning deformation based on feature extraction operation of a tubular convolution kernel, and then a three-vision feature fusion strategy is carried out to further guide a model to supplement attention to key features from different angles; the encoder part models global and multi-scale features through a Mama architecture, and keeps efficient in training and reasoning processes, and finally enables the network to have a more complete segmentation effect on a CBCT image of a tubular structure.

The invention provides a domain adaptation cross-modal medical image segmentation method and device based on boundary comparison, equipment and a medium, and relates to the technical field of medical image segmentation. The method comprises the following steps: respectively acquiring a source sample and a target domain image from a source domain data set and a target domain data set; the first image segmentation model is used as a teacher model, and weights are distributed for the pseudo labels of the target domain images; a training sample is obtained through bidirectional cross-domain cutmix; performing supervision and self-training by taking a weight learnable first model as a student model; combining supervision loss and comparison loss during training to pull a query sample to a prototype positive sample and push a boundary negative sample as a target optimization parameter, and obtaining a second image segmentation model; and finally, segmenting a second modal medical image by using the model. The method has significant advantages in cross-modal medical image segmentation, and the segmentation performance can be effectively improved.

The invention provides a brain tumor image accelerated diffusion network segmentation method based on fuzzy learning, and belongs to the technical field of medical image intelligent segmentation. The technical problem of low segmentation accuracy caused by fuzzy texture and boundary disappearance of the brain tumor image is solved. According to the technical scheme, the method comprises the following steps of S1, performing data preprocessing on a brain tumor image data set; s2, performing pre-segmentation on the brain tumor image by using a 3D U-Net network, and performing iterative noise addition on the obtained segmented image to enable the segmented image to become a pure noise image; s3, performing iterative denoising on the pure noise image, and learning a denoising process by using a fuzzy U-Net network; and S4, stopping the denoising process in advance, and segmenting the segmented image by using a 3D U-Net network to obtain a final segmentation result. The method has the advantages that the segmentation accuracy is high, decision support is provided for segmentation of the brain tumor image, and convenience is provided for diagnosis of doctors.

An underwater in-situ sea cucumber volume measurement method based on binocular vision and electronic equipment belong to the field of image processing, are used for solving the problem of keeping in-situ sea cucumber volume measurement, and are technically characterized in that a sea cucumber image is detected in underwater images collected by a binocular camera to obtain a sea cucumber image detection frame; segmenting a sea cucumber image detected in the image according to the sea cucumber image detection frame, and calculating mask information of the sea cucumber on an image plane; mapping the mask information to a global point cloud constructed by binocular information, and obtaining point cloud data of the sea cucumber; performing grid reconstruction on the point cloud data of the sea cucumber to obtain a continuous triangular grid; according to the method, the continuous triangular grids are converted into voxel representation, the number of non-zero elements in the voxel grids and the volume of each element are counted, the volume of the grids is estimated, and the volume of the sea cucumbers is obtained, so that the volume of the in-situ sea cucumbers can be accurately evaluated, and more accurate data support is provided for scientific cultivation of the sea cucumbers.

The invention discloses a cross-model knowledge distillation method for a remote sensing semantic segmentation task, and relates to a cross-model knowledge distillation method. The method aims at solving the problems that an existing remote sensing image semantic segmentation model is large in parameter quantity and large in resource consumption. The method comprises the steps of selecting a student model and a teacher model which are complementary in ability, selecting a data set to train the teacher model, and using a feature map output by the trained teacher model as knowledge to be input into the student model; carrying out attention weighting on the feature map input into the student model according to channels, and calculating loss; the knowledge transmitted by the teacher model is decoupled into a target area and a non-target area, the two independent target areas are subjected to prediction and knowledge distillation, and loss is calculated; and carrying out weighted summation on the loss of the semantic segmentation of the student model and the loss of the above steps, calculating the total loss, updating the parameters of the student model through the total loss, and finally completing the training of the student model. The invention belongs to the technical field of remote sensing image processing.

The invention belongs to the field of mass spectrum imaging, and particularly provides an image segmentation method for a mass spectrum imaging image of an ESI source, and the method comprises the steps: (S1) dividing the mass spectrum imaging image into a plurality of mass spectrum imaging channels according to a mass-to-charge ratio; (S2) selecting a mass spectrum imaging channel where target ions are located, and preprocessing each pixel; (S3) carrying out preliminary segmentation on the spectrogram data; (S4) performing interval division on the mass spectrum data of the channel where the target ions of each pixel are located, calculating the lower area of each interval, selecting the central mass-to-charge ratio of each interval as the abscissa of the intensity value of each interval, and setting the intensity value of the interval with the ratio of the central mass-to-charge ratio to the highest intensity value lower than a set value as 0; (S5) analyzing, training and optimizing the transformed mass spectrum data; and (S6) the optimal combination model obtained in the step (S5) is used for classification of other pixel points of the mass spectrum imaging image, and an image segmentation result is obtained. The method has the advantages of accurate segmentation and the like, and is applied to mass spectrum imaging of the ESI source.

The invention discloses a medical image segmentation self-pre-training method and system based on a difficult sample mining mask auto-encoder, and the method comprises the steps: collecting whole-body CT scanning images of a plurality of subjects, carrying out the preprocessing of the CT scanning images, and obtaining a plurality of three-dimensional sub-images with the same size; carrying out self-pre-training on a deep learning model HPM with an attention mechanism, wherein the deep learning model HPM comprises a block embedding layer, a position embedding layer, a student VIT network and a teacher VIT network; after training is finished, an encoder of the student VIT network is connected with a UNETR decoder, and a segmentation network model is constructed; and training the segmentation network model, inputting a new three-dimensional sub-image into the segmentation network model after training is completed, and finally outputting a 42-class segmentation result. According to the method, the accuracy of medical image segmentation can be improved through difficult sample mining and a self-pre-training mechanism.

The invention discloses a blood vessel segmentation method and device based on large model fine tuning, equipment and a medium, and the method comprises the steps: obtaining a blood vessel image, carrying out the preprocessing of the obtained blood vessel image, and obtaining a training set; performing fine tuning on a pre-trained image segmentation model by adopting the training set, so that the image segmentation model learns related features of blood vessel segmentation; wherein the low-rank adaptive fine tuning technology is adopted to perform fine tuning on the image segmentation model: two parallel low-rank matrixes are inserted into a Transform layer in the image encoder, the input is processed by the two low-rank matrixes in sequence, and the processing result and the original output are added to obtain the final output. According to the method, the defects of a large model in a blood vessel segmentation task are overcome through efficient parameter fine tuning and a multi-scale feature fusion module. Accurate blood vessel segmentation can be realized under limited annotation data, the difficulty and cost of medical image segmentation tasks are greatly reduced, and the method can be widely applied to the technical field of medical image processing.

The invention discloses an ocean mesoscale vortex segmentation method based on edge attention, and relates to the field of semantic segmentation. The problems that an existing model is poor in vortex edge information extraction effect, and the shape and the position of the segmented ocean mesoscale vortex are not accurate enough are solved. The method specifically comprises the following steps of (1) obtaining sea level abnormal data SLA, performing visualization processing on the SLA to generate a sea mesoscale vortex image data set, performing labeling, dividing image data into a training set, a verification set and a test set, and performing preprocessing by using image enhancement operation; (2) creating a network model, inputting the preprocessed ocean mesoscale vortex image data set into an ocean mesoscale vortex segmentation network based on edge attention, namely an EGA-AFFUNet model, for training, and segmenting ocean mesoscale vortexes in the ocean mesoscale vortex image data set; and comparing a test result with the vortex label data set on the test set to obtain an evaluation index.

The invention discloses a pavement crack segmentation method based on adaptive feature extraction and attention fusion, and relates to the technical field of image recognition, and the method comprises the steps: S1, obtaining a pavement crack data set which comprises a pavement crack training set, a pavement crack verification set and a pavement crack test set; and S2, performing data expansion on the pavement crack training set in the S1, not performing data expansion on the pavement crack verification set and the pavement crack test set to obtain a pavement crack sample set, and selecting a training crack detection network by using the pavement crack verification set obtained in the S1. A content-guided attention mechanism (CGA) is improved by using a convolution and attention fusion module (CAFM), the CGA is added into a content-guided attention fusion module (CGAFUS), a convolution and attention fusion mechanism (CAFMFUSon module is proposed, the model can adaptively focus on a complex topological structure of the crack, the adaptive feature extraction capability of the model on the pavement crack is enhanced, and the pavement crack feature extraction efficiency is improved. Moreover, the features of different levels can be fused more effectively, and the precision and robustness of crack detection are improved.

The invention discloses a multi-modal brain tumor image segmentation system based on a sparse attention mechanism, and relates to the technical field of medical image processing, and the system comprises a data obtaining unit which is used for obtaining multi-modal brain tumor data and carrying out the preprocessing of the multi-modal brain tumor data to obtain multi-modal data; the feature extraction unit is used for extracting a first specific feature Fm of each mode in the multi-mode data based on a first encoder; the feature fusion unit is used for obtaining a first feature f1 based on the first specific feature Fm and a first fusion token Ff; based on a modal sparse mask fusion Transform model, interacting the first feature f1 to obtain a second feature Z, based on the second feature Z, extracting a weight IMm of each modal, based on the weight IMm, obtaining a third feature # imgabs0 #, based on spatial dimensions, and based on the third feature # imgabs0 #, obtaining a second feature Z; the prediction unit is used for inputting the fourth feature # imgabs3 # into a brain tumor segmentation model to obtain a first loss L1, obtaining a prediction segmentation result based on the first loss L1, and obtaining a prediction segmentation result based on the prediction segmentation result; the problems that an existing segmentation mode based on shared feature representation is insensitive to brain tumor information, information redundancy occurs in brain tumor feature fusion when modals are lost, and important brain tumor information is ignored can be solved.

The invention discloses an inter-frame image segmentation method and device based on a recurrent neural network, and a medium, and relates to the technical field of image processing, and the method comprises the steps: extracting the local feature information of a current continuous inter-frame image through a convolutional neural network; constructing a geometric transformation model through the Lie group theory according to the local feature information, and extracting geometric change information of the target object in the image based on the geometric transformation model; based on the geometric change information, fusing the dynamic information of the adjacent inter-frame images through a recurrent neural network to extract time sequence features between the adjacent inter-frame images; and performing segmentation prediction on each inter-frame image in the current continuous inter-frame images through a segmentation network according to the time sequence characteristics. According to the method, the Lie group is used for capturing continuous geometric transformation between image frames, and the accuracy of the shape and the edge of a target object is kept, so that the segmentation method has better robustness for geometric transformation, and particularly, the method is excellent in performance when processing complex backgrounds and rapid motion scenes.

The invention provides a pavement crack detection method and system. The method comprises the following steps: acquiring a plurality of pavement crack images; a Swinin-Unet model is constructed; determining the total loss of the Swinin-Unet model by using the plurality of pavement crack images; using the total loss of the Swinin-Unet model to train the Swinin-Unet model, and using the trained Swinin-Unet model as a pavement crack detection model; and inputting a to-be-detected pavement crack image into the pavement crack detection model to obtain a pavement crack detection result output by the pavement crack detection model. According to the method, the comprehensive capture capability on the global and local features of the crack and the segmentation effect on crack details are improved.

The invention relates to the field of computer vision, and discloses a few-sample semantic segmentation method based on image-text fusion, comprising the following steps: S1, collecting and preprocessing data sets, grouping the data sets according to categories, each group comprising a plurality of support images and query images; s2, extracting multi-scale image features by using a pre-trained deep neural network; s3, extracting text features prompted by a preset text by using a text encoder; s4, fusing the extracted text features with the image features; s5, mining the correlation between the support image features and the query image features by using an attention mechanism; and S6, performing up-sampling on the fused image features, and decoding to generate a segmentation prediction result. According to the method, semantic information is supplemented through image-text fusion and text prompt, and the accuracy and performance of few-sample semantic segmentation are remarkably improved.

A medical image segmentation method based on multi-extended recursive attention belongs to the technical field of image segmentation, and comprises the following steps: a coding part: coding input features by a multi-extended recursive attention block and a feature merging layer; in the decoding part, firstly, the feature data of the multi-extended recursive attention block encoding part are input into a decoder; secondly, the decoder gradually recovers the original resolution of the image through up-sampling, and semantic information obtained through up-sampling and features output by the encoder of the first part are subjected to feature interaction through jump connection; thirdly, inputting the interacted features into the decoding part of the recursive attention block, and restoring the original information of the image to the maximum extent; and finally, through a linear mapping layer, obtaining a pixel-level segmentation prediction result according to the extracted image feature marks. By adopting the method, the model can show better overall performance and robustness in a segmentation task, and the method has important significance in practical application in the fields of medical images and the like.

The embodiment of the invention discloses an image segmentation method and device for a small-scale component, equipment and a storage medium. The method comprises the steps of obtaining a to-be-segmented image with a to-be-segmented small-scale component; identifying and positioning a small-scale component in the to-be-segmented image by using a multi-scale analysis technology to obtain a target image area of the small-scale component; performing boundary optimization processing on the target image area by using a preset adaptive threshold adjustment mechanism to obtain a preliminary image segmentation result; correcting the preliminary image segmentation result by using pre-collected priori knowledge and context information of the small-scale component to obtain a corrected image segmentation result; performing morphological operation processing on the corrected image segmentation result to obtain an optimized image segmentation result; and re-projecting the optimized image segmentation result into the original image coordinate system to generate a final image segmentation result of the small-scale component. Through the above mode, the precision and reliability of image segmentation of small-scale components are improved.

The invention discloses an erosion gully segmentation method based on multi-scale edge perception, and relates to the technical field of remote sensing image target extraction. The invention aims to solve the problem that the boundary extraction of the erosion gully by a common neural network algorithm is not clear due to the complicated geographical environment and characteristics of the erosion gully. According to the erosion gully segmentation method based on multi-scale edge perception, a boundary detection module and a multi-scale decoder are adopted, the edge detection module is used for extracting edge features of an erosion gully, the capability of extracting boundary details of the erosion gully is enhanced, and the segmentation precision of the whole erosion gully is improved; the multi-scale decoder decodes the features of the encoder and the boundary detection module, context feature information extracted by the encoder is fully utilized, the loss of detail feature information is inhibited, and the segmentation capability for fuzzy gullies and the overall segmentation precision are improved.

The invention relates to an intelligent building extraction method based on a remote sensing image. The method comprises the following steps: S1, obtaining a high-resolution data set; s2, designing a remote sensing image building semantic segmentation network based on a coding-decoding structure, wherein the remote sensing image building semantic segmentation network comprises a feature extraction skeleton network and a semantic segmentation decoder; s3, a remote sensing image building DSM estimation network based on the generative adversarial network is designed and comprises a DSM generator and a DSM discriminator, and the DSM generator comprises a DSM generator encoder and a DSM generator decoder; s4, designing a feature fusion and reinforcement module; s5, designing a loss function; s6, training and optimizing a remote sensing image building intelligent extraction network according to the high-resolution data set and the loss function; and S7, performing building intelligent extraction based on the remote sensing image through the trained remote sensing image building intelligent extraction network. According to the method, the problem of high DSM acquisition cost is solved, and the performance of extracting the building in the remote sensing image is effectively improved.