139 results about "Remote sensing image segmentation" patented technology

The invention discloses a remote sensing image semantic segmentation method based on CNN-Transform-SAM dynamic collaboration and scene adaptation, and a constructed remote sensing image segmentation network comprises a scene attribute analysis module, a dynamic backbone decision module, a CNN-Transform expert sub-network, a cross-modal feature calibration module, a multi-modal prompt generator and an SAM adaptive general sub-network. And all the modules realize dynamic collaboration through data interaction. Wherein the scene attribute analysis module analyzes image resolution, spectrum and target scale attributes, the dynamic backbone decision-making module matches the optimal feature extractor according to the image resolution, spectrum and target scale attributes, the CNN-Transform expert sub-network generates small target enhanced adaptive masks through multi-scale interaction and up-sampling refinement, the cross-modal feature calibration module optimizes the masks and semantic distribution to generate alignment masks, and the cross-modal feature calibration module outputs the alignment masks. And the multi-modal prompt generator generates a multi-modal optimization prompt set based on the alignment mask, and guides the SAM adaptive universal sub-network to complete segmentation. The method effectively solves the problems of poor small target segmentation, fuzzy boundary and lack of remote sensing exclusive semantic priori in the prior art.

The invention discloses a remote sensing image segmentation method fusing frequency modulation and spatial perception, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and generating a standardized input image; the image is input into a multi-scale frequency domain enhanced feature extraction network, features are extracted step by step according to a plurality of feature levels, each level realizes frequency adaptive semantic enhancement through frequency domain modulation transformation and spatial feature fusion, and deep feature expression is enhanced through feedforward neural network modeling and residual connection output and cross-level residual fusion introduction; the final multi-scale features are decoded through a decoding module, the spatial resolution is recovered, and a pixel-level segmentation result is generated; and constructing a composite loss function containing classification errors, boundary perception and frequency consistency items, and carrying out optimization training on the network. According to the method, semantic complementarity of a remote sensing image in a frequency domain and a space domain is fully mined, so that segmentation precision and robustness of a ground object target in a complex scene are improved, and the method has good generalization ability and engineering practicability.

The invention discloses a remote sensing image multi-scale segmentation method based on frequency spectrum information processing and Mama spatial modeling, and the method comprises the steps: carrying out the preprocessing of a remote sensing image, and obtaining an input image; inputting the input image into the trained remote sensing image segmentation model; the remote sensing image segmentation model comprises an initial convolutional layer, a spectral domain information processing unit branch, a Mama layer branch and a segmentation head; performing feature extraction on the input image through the initial convolutional layer to obtain initial image features; combining a frequency spectrum domain information processing unit branch and a Mama layer branch, and performing feature extraction on the initial image features to obtain fusion features; and performing up-sampling decoding and pixel-level classification on the fusion features through the segmentation head, and outputting a multi-scale segmentation result corresponding to the remote sensing image. According to the method, the spectral domain and the spatial domain are combined for feature extraction, and the common problems of fuzzy details, unclear boundaries, insufficient multi-scale target expression and the like in remote sensing image segmentation are solved.

The invention discloses a remote sensing image segmentation method and device based on a large language model, a storage medium and electronic equipment. The method comprises the following steps: acquiring a remote sensing image and a natural language corresponding to the remote sensing image; performing multi-scale feature extraction and fusion on the remote sensing image to obtain a multi-scale fusion feature map; encoding the natural language to obtain an embedded vector, processing the multi-scale fusion feature map through a visual token compression mechanism to obtain a compressed image feature, and aligning the embedded vector and the compressed image feature to obtain a cross-modal semantic feature; and generating initial masks based on the multi-scale fusion feature map and the cross-modal semantic features, and performing weighted fusion on all the initial masks to obtain a final semantic target mask. According to the method, a remote sensing image segmentation task with high semantic complexity can be realized, and the method has remarkable intellectualization and practicability advantages.

The invention relates to a remote sensing image segmentation method and system based on land utilization, and the method comprises the steps: collecting continuous periodic remote sensing images of the same region, marking a land type and a seasonal factor vector, and determining whether to carry out zoning or not according to a latitude span; then identifying the boundary of the miniature land parcel, calculating the boundary identification degree, delimiting a protection area, respectively calculating the seasonal factor vectors of the non-protection area and the protection area, and fusing the seasonal factor vectors into a global seasonal factor; secondly, preliminarily segmenting the image by adopting a fuzzy C-means clustering model in combination with a global seasonal factor, and finally carrying out image re-segmentation and verifying the accuracy in combination with a plot seasonal feature and boundary recognition degree correction model; according to the method, through operations such as fine seasonal factor vector construction, latitude zoning, protection area delimiting and multi-feature fusion, the shadow area land type classification accuracy is effectively improved, the influence of seasonal variation and boundary blur in a complex earth surface scene can be reduced, and the robustness and adaptability of the model are enhanced.

The invention discloses a remote sensing image segmentation method and system fusing stage perception and a multi-dimensional orientation mechanism, and belongs to the technical field of remote sensing image processing, and the method comprises the following steps: S1, carrying out the preprocessing of a to-be-segmented remote sensing image; s2, inputting the preprocessed to-be-segmented remote sensing image into a segmentation model of a fusion stage perception and multi-dimensional orientation mechanism to obtain a segmentation result graph of the to-be-segmented remote sensing image; wherein the segmentation model fusing the stage perception and the multi-dimensional orientation mechanism comprises a plurality of stage perception intensifiers and a multi-dimensional orientation cyclic key value module. According to the method, accurate segmentation of multi-scale, multi-direction and multi-category targets of the remote sensing image is realized on the basis of keeping light weight of the model.

The invention provides a remote sensing road image segmentation method, device and equipment based on geometric reasoning guidance, and relates to the technical field of remote sensing image segmentation. The method comprises the following steps: acquiring a remote sensing road image, and inputting the remote sensing road image into an encoder for spatial feature extraction and modulation to obtain multi-scale features after feature modulation; road geometric shape reasoning is carried out on the multi-scale features after feature modulation, a continuous geometric prior field is generated, and a reasoning fusion feature map is output; performing dynamic trajectory sampling and aggregation on the inference fusion feature map based on the geometric prior field, and outputting long-range connected aggregation features; and performing decoding reconstruction on the aggregated features and shallow features in the multi-scale features, and outputting a binary road segmentation mask, namely a road image segmentation result. According to the method, the technical defects of road topology fracture, edge fuzzy adhesion, dynamic sampling drift and the like in the existing remote sensing road segmentation technology can be solved.

The invention belongs to the technical field of remote sensing image segmentation, and particularly relates to a double-branch water body small target image segmentation method based on feature efficient interaction, which comprises the following steps: preparing a data set, constructing a network model, training the network model, selecting a proper loss function and evaluation index, and determining a segmentation model. According to the multi-scale detail feature interactive aggregation encoder, efficient fusion of multi-scale features and effective supplement of implicit relative position encoding information are achieved; the long-distance feature efficient capture encoder increases the edge segmentation effect on the small target image of the water body by processing features along a specific space direction; the cavity space convolution pyramid module based on the large convolution kernel is used for improving the ability of the model to learn a large-scale effective receptive field; the non-significant feature extraction module is used for filling up the deficiency of non-significant features. The whole network adopts a learning strategy of parallel connection of global and local features and series connection from a large scale to a small scale, and effectively extracts different scale information of the water body image.

The invention discloses a remote sensing image segmentation method based on semantic space interaction and edge guidance, which relates to the field of remote sensing image segmentation, and comprises the following steps: carrying out multi-level standard convolution block processing and semantic space interaction module processing on a preprocessed remote sensing image by utilizing a feature extraction network, extracting basic features, and carrying out semantic space information decoupling; and outputting multi-layer enhanced features, performing multi-level standard convolution block and up-sampling processing on the features through a feature fusion network, fusing the features through jump connection and combining a classification head to output a segmentation probability graph. According to the method, feature expression is enhanced through semantic space interaction decoupling, boundary recognition is enhanced through an edge supervision mechanism, and multi-scale fusion and joint loss optimization are carried out, so that the segmentation precision and boundary coherence of the remote sensing image in a complex scene are remarkably improved.

The invention belongs to the technical field of remote sensing image processing, particularly relates to an urban remote sensing image segmentation method and system based on bidirectional coordinate attention and multi-scale adaptive feature fusion, and provides a remote sensing image semantic segmentation neural network architecture taking an attention re-calibration module as a decoder core. Wherein the encoder path gradually extracts multi-scale feature representation through cascaded residual convolution blocks and down-sampling operation to form a feature pyramid of which the spatial resolution is reduced step by step and semantic information is enhanced step by step; the decoder path gradually recovers the spatial resolution through cascaded up-sampling and feature refining operations to generate a precise segmentation mask; a space-channel dual attention re-calibration module oriented to a decoding stage is provided, through explicit coding of space coordinate direction information and combination of global channel dependence modeling, features beneficial to semantic discrimination are adaptively enhanced in the feature fusion and resolution recovery process, and therefore segmentation precision and consistency are improved.

The invention discloses an EAGLE-Net remote sensing image segmentation method, which comprises the steps of extracting multi-scale features of an input image, and obtaining low-level features of spatial details and high-level features of semantic information; the low-level features of the space details and the high-level features of the semantic information are input into an attention gating module, space-channel two-dimensional attention weights are generated, and weighting processing is conducted on the low-level features of the space details and the high-level features of the semantic information; inputting the weighted high-level features into a dynamic void space pyramid, predicting multiple groups of void rates based on global information, and generating enhanced semantic features through multi-scale void convolution fusion; splicing and decoding the weighted low-layer features and the enhanced semantic features to obtain a segmented prediction map; and performing edge detection on the segmented prediction map to generate an edge prediction map. According to the method, noise can be suppressed, key signals can be enhanced, large targets and small targets can be adaptively covered, and object boundaries can be accurately recovered by means of edge supervision.

The invention discloses a remote sensing image segmentation method based on frequency domain global channel perception and cross-channel attention fusion, and relates to the technical field of remote sensing image processing. Comprising the steps of inputting remote sensing image features, mapping the remote sensing image features to a frequency domain, performing amplitude enhancement and phase channel transformation, reconstructing frequency domain features, and inversely transforming the frequency domain features back to a spatial domain to obtain frequency domain enhancement features; a cross-channel attention mechanism is applied to the frequency domain enhanced feature, a channel dependency relationship is modeled through query, key and value interaction, and the frequency domain enhanced feature is fused to obtain a fused feature; and converting the fusion feature into a segmentation image, and performing model optimization through a multi-task loss function. According to the method, the boundary fineness and small target detection can be considered while the global consistency is ensured, meanwhile, the calculation complexity is reduced, and the practicability and the engineering feasibility of the method are improved.

The invention discloses a remote sensing image segmentation method based on multi-scale gating bottleneck convolution scanning, and relates to the field of remote sensing image segmentation, and the method comprises the steps: inputting a remote sensing image, and extracting initial features through an initial convolution layer; carrying out multi-level down-sampling processing on the initial features through an encoder, and carrying out feature enhancement on each level by adopting a multi-scale gating bottleneck convolution scanning unit in sequence to obtain multi-level encoding features; introducing a semantic bridging attention unit at the deepest layer feature of the encoder to carry out global semantic modeling and residual fusion to obtain an enhanced deep layer semantic feature; performing up-sampling processing step by step in a decoder, and fusing the up-sampled feature with the coding feature of the corresponding level to obtain a multi-level fusion feature; and inputting the fusion feature output by the decoder into the segmentation head to obtain a prediction probability graph. According to the method, the segmentation continuity and category discrimination robustness of slender targets, complex boundaries and small-scale ground features in the remote sensing image are improved.

The invention belongs to the field of deep learning technology and remote sensing image segmentation, and particularly relates to a remote sensing image segmentation method based on Transsubnet edge information enhancement and multi-dimensional feature perception, and the method comprises the steps: S1, preparing a data set; s2, constructing remote sensing picture text description; s3, constructing and training a remote sensing image segmentation model; and S4, storing and testing the model. The invention designs a multi-modal feature extraction method based on parallelism of a sampling branch and a text feature extraction branch under edge feature compensation. The residual error mixing axial attention module is used for forming a transformer structure; and a text-picture multi-dimensional feature fusion enhancement module and a decoder part are embedded. According to the method, the ground feature identification capability can be improved through a text and picture multi-modal feature enhancement strategy, the segmentation boundary and small target object feature information is enhanced, more fine-grained features are reserved, the cross-regional long-distance dependency relationship is better captured, the common gradient disappearance problem in a deep network is relieved, and the method is suitable for large-scale popularization and application. And meanwhile, the small sample data set segmentation effect is improved.

The invention discloses a remote sensing image segmentation method based on double-sequence significance guidance and space gating, and belongs to the field of computer vision. According to the method, firstly, a training data set is constructed, features are extracted through a double-branch encoder, the features comprise a global sequence scanning branch and a saliency spiral scanning branch, the global sequence scanning branch adopts horizontal and vertical forward and backward scanning to reserve global layout information, and the saliency spiral scanning branch positions an initial anchor point through a saliency map to generate a bidirectional spiral path and focus key area features. In the encoder, deep fusion of the convolutional neural network and the Mama model is realized through a space gating state transition mechanism, and the Mama hidden state is guided to be updated by using space features. The decoder completes double-branch feature calibration and alignment through a multi-stage cross-scanning feature calibration module, reinforces semantic consensus and optimizes detail differences, and adopts a three-head supervision and consistency constraint strategy combined training model. According to the method, the segmentation precision and efficiency of the high-resolution remote sensing image complex ground feature are remarkably improved.

The invention discloses a remote sensing image segmentation method based on multi-scale wavelet transform and Mama. The method comprises the following four steps: firstly, carrying out preprocessing and division on an ISPRS Potsdam data set and a Vaihingen data set; then constructing a segmentation network, wherein the network comprises a local detail extraction branch, a spatial semantic extraction branch, a cross-branch feature fusion part and a decoder; training and optimizing the network by using the training set; and finally, performing segmentation reasoning on a test image by using the trained model. According to the method, the high-frequency detail extraction capability of the image is enhanced through multi-scale wavelet transform, the long-distance dependency relationship is modeled by using the visual state space block, and effective fusion of the features is realized through the double-branch fusion module, so that the feature extraction capability and the semantic segmentation precision are improved, and meanwhile, the training efficiency and the stability are optimized.

The invention belongs to the technical field of remote sensing image segmentation, and particularly relates to a self-adaptive multi-view lightweight collaborative image segmentation network method, which comprises the following steps of: 1, acquiring a multi-view remote sensing image data set; step 2, inputting the image data into a self-adaptive multi-view feature extraction module, fusing information of different views, and automatically screening key features through a gating mechanism to suppress redundancy; step 3, performing multi-scale context modeling and detail enhancement on the features by a double-domain adaptive lightweight attention module, and improving the expression ability of space and channel dimensions; and step 4, the channel adaptive fusion module performs weighted integration on the multi-source features to realize semantic consistency enhancement and robustness improvement. Aiming at a multi-view remote sensing image segmentation task, innovative improvement is carried out in view collaborative modeling, attention optimization design, a feature fusion mechanism and the like, the segmentation precision, the calculation efficiency and the generalization ability are remarkably improved while the lightweight of the model is guaranteed, and the method is suitable for remote sensing intelligent interpretation application in a resource-constrained environment.

The invention relates to a remote sensing image segmentation method and device, electronic equipment and a medium, and belongs to the technical field of image recognition, and the method comprises the steps: obtaining a to-be-segmented remote sensing image and a corresponding text; inputting a to-be-segmented remote sensing image and a corresponding text into the completely trained remote sensing image segmentation model to obtain a predicted dynamic query vector of the to-be-segmented remote sensing image; determining a first segmentation mask based on the predicted dynamic query vector, and determining target segmentation of the remote sensing image to be segmented based on the first segmentation mask; and training a complete remote sensing image segmentation model, wherein the model comprises a visual backbone network, a semantic mutual guidance alignment module, a cross-modal counting prediction module and a dynamic query generation module. According to the method, the target segmentation precision of the remote sensing image in various different language type scenes is improved.

The invention relates to the technical field of image processing, and discloses a man-made forest remote sensing image segmentation method, electronic equipment and a computer readable storage medium, the method introduces MMF, performs joint modeling on vegetation indexes such as NDVI, EVI and the like and RGB images so as to improve the ability of a model to distinguish a man-made forest from other ground features, and introduces KAN-HGC so as to improve the efficiency of the model to distinguish the man-made forest from other ground features. A graph structure is adaptively constructed by combining a space structure and feature similarity, so that the recognition capability of the model on a boundary region is improved, and GIGA is introduced, and key region features are enhanced through multi-dimensional attention, so that background interference is effectively inhibited, and the recognition accuracy of the model in a complex scene is improved; besides, an FReSCO optimizer is provided, a boundary enhancement and spectrum adjustment mechanism is introduced in training, and the adaptability and cross-scene generalization ability of the model to spectrum differences are improved.

The invention discloses a remote sensing image segmentation method combining multi-scale feature enhancement and an SAM large model, and the method comprises the steps: constructing a target segmentation model which comprises an image processing module and a mask decoder; obtaining a target remote sensing image, and carrying out image coding processing on the target remote sensing image based on the image processing module to obtain an image embedding representation; extracting an intermediate layer output result of the image processing module, and obtaining a prompt embedding representation based on the intermediate layer output result; inputting the image embedding representation and the prompt embedding representation into the mask decoder to obtain a plurality of binary mask prediction results; and segmenting the target remote sensing image based on the plurality of binary mask prediction results to obtain a target segmented image. According to the method, multi-level coding processing is carried out on the low-resolution remote sensing image, so that the segmentation precision and generalization capability of classifying the types of the surface features of the low-resolution remote sensing image are effectively improved.

The invention discloses a remote sensing image segmentation method and system based on a fine screening double-domain attention mechanism, and belongs to the technical field of remote sensing image processing. A multi-level feature extraction network with a fine screening double-domain attention unit as a core is constructed, and pyramid-like cross-layer residual connection is fused; semantic expression and spatial distribution perception capability in the remote sensing image are remarkably enhanced, a fine screening double-domain attention unit is introduced into each layer of coding module, a multi-layer feature fusion mechanism is matched, sufficient modeling of texture, structure and context information of the remote sensing image under different scales is achieved, and remote sensing image segmentation is completed on the basis.

The application discloses a lane line segmentation method and device based on spatial context and a storage medium, relates to the technical field of deep learning remote sensing image segmentation, and extracts high-resolution features and channel correlation of an input remote sensing image by using an improved high-resolution feature extraction network HRNet as a backbone network. Secondly, a spatial context attention module is designed to enhance the position and shape features of lane lines. Finally, in terms of data processing, the algorithm combines online enhancement and offline enhancement operations, increases sample diversity, and enhances the generalization ability of the model. The application of the method to lane line segmentation tasks on remote sensing images can significantly improve the segmentation accuracy and classification accuracy of various lane lines, and the segmentation effect is better than that of existing algorithms, which has important significance for remote sensing image information analysis.

This invention provides a method for building extraction from remote sensing images that integrates convolutional neural networks (CNNs) and Transformers, relating to the intersection of remote sensing image segmentation and computer vision technologies. This method involves creating a remote sensing image dataset from acquired remote sensing images, labeling each image individually, and dividing the dataset into training, validation, and test sets. The preprocessed remote sensing images are then preprocessed to increase data diversity. Feature extraction is performed on the preprocessed images to collect feature maps containing building information and obtain global features of the image. This method can significantly reduce false positives and false negatives for small target buildings, improve the completeness of segmentation for large target buildings, and mitigate boundary blurring caused by insufficient extraction of target building edge information.

The invention relates to the technical field of image segmentation, and discloses a remote sensing image segmentation method based on global semantic progressive enhancement and boundary assistance, comprising the following steps: constructing a semantic segmentation network of an encoder-decoder structure, the encoder adopting ConvNeXt as a backbone network; a global semantic progressive enhancement network (IEGN) is embedded in the encoder, and the global semantic progressive enhancement network (IEGN) comprises a local-global feature enhancement module (LGEB); an interpretable dynamic channel transformation unit (EDCT) is embedded in the encoder; a boundary-assisted semantic enhancement module (BAE) is embedded between shallow and deep features of the encoder; an adaptive frequency enhancement module (FAR) is introduced in the decoder or feature fusion stage. According to the method, the global semantic progressive enhancement network and the local-global feature enhancement module are introduced, so that implicit global modeling of large kernel convolution and explicit remote dependency capture of a self-attention mechanism can be effectively fused.

The application discloses a remote sensing image segmentation method and device, which is applied to the technical field of remote sensing image segmentation and comprises the following steps: performing segmentation processing on a remote sensing image by using an SCFEMamba model to obtain an outline map of a ground object area, wherein the model is obtained by replacing two visual state space blocks stacked in the first three decoders of a UNetMamba model with a feature enhancement block and at least one improved decoding block connected in sequence; in the improved decoding block, input of the improved decoding block is divided into a first input and a second input, convolution processing is performed on the first input to obtain a convolution branch feature map, visual state space processing is performed on the second input to obtain a state branch feature map, and output of the improved decoding block is obtained according to the convolution branch feature map, the state branch feature map and the input of the improved decoding block; and the feature enhancement block performs feature enhancement processing on output of the last improved decoding block. The application effectively improves the segmentation precision of the remote sensing image.

The application discloses a kind of based on paradigm-crossing feature fusion and alignment remote sensing image segmentation method and system, comprising: the initial feature is extracted to the input remote sensing image and is preprocessed;Input paradigm-crossing feature fusion and alignment network, through sparse channel enhancement and spatial alignment and spatial pixel refining and channel alignment, fusion multi-modal, cross-scale remote sensing image structure information, obtain first stage fusion feature;Input multi-stage paradigm-crossing enhanced feature extraction network, through multi-level information interaction and dynamic gate mechanism, fusion local details and global context information, gradually extract out the joint feature map of semantic and spatial structure collaborative expression;Through segmentation head, generate final semantic segmentation result, and calculate composite loss based on real label;The application is by constructing multi-stage feature extraction network and paradigm-crossing feature alignment mechanism, effectively fusion local texture, spatial context and multi-modal information, while guaranteeing the computing efficiency, strengthen segmentation performance.

The application discloses a kind of based on attention enhancement and dense multi-scale ground feature classification network model, to solve the problem of insufficiently fine remote sensing image segmentation result caused by " same spectrum different things " and " same thing different spectrum " phenomenon.The method is based on DeepLabv3+ network, in order to better process remote sensing image, the deficiency of its encoder and decoder stage is improved in model structure.In the encoder stage, the application designs a kind of attention enhancement dense hollow pyramid pooling to replace the original hollow space pyramid pooling structure, and simultaneously, the double attention mechanism is introduced in global feature, which can help remote sensing image to achieve balance between feature representation capability and spatial positioning accuracy.In the encoder stage, the multi-level feature map of image is used to upsample the image by using dense connection mode.

This invention discloses a remote sensing image segmentation method based on an integrated cross-attention mechanism. The method employs a deep residual network ResNet101 to extract features from RGB remote sensing images, obtaining an initial feature map. A cross-attention mechanism is introduced, using the original image and the initial feature map as input, to mine the regional associations within the RGB remote sensing image, enhance key region features for target recognition, and perform feature enhancement on the initial feature map, outputting a focused feature map. A deep learning model, DeepLabV3, is used to extract multi-scale features from the focused feature map. The Awfully Hollow Spatial Pyramid Pooling (ASPP) module captures contextual features under different receptive fields and fuses them with low-level detail features from ResNet101, resulting in a final feature map with rich context and clear boundaries, outputting the semantic segmentation result. This invention improves the segmentation capability of complex scenes through the cross-attention mechanism, and by utilizing image space and contextual information, it can effectively improve the segmentation accuracy of complex urban and rural features.

The application discloses a visual model training method, a remote sensing image segmentation method and related devices, and relates to the technical field of computers. Specifically, the application first performs model pre-training on a visual model on a pre-training data set. The pre-trained visual model has good representation capability for remote sensing images. Then, model parameters of the pre-trained visual model are configured as initial parameters of a basic model, and the pre-configured basic model is trained on data sets corresponding to related tasks and irrelevant tasks of a target task, so as to obtain a pre-trained basic model. Finally, the pre-trained basic model is trained on a data set corresponding to the target task. Since the pre-trained basic model has general representation capability, the pre-trained basic model can be optimized by using a small amount of training data corresponding to the target task, so that the processing capability of the model for the target task is improved, and the target task is reduced to depend on the training data of the target task.

The application discloses a kind of remote sensing image segmentation error correction method, system, medium and equipment in the technical field of deep learning, to solve the optimization problem of remote sensing image segmentation error correction repair. Method includes: the remote sensing image data obtained is carried out data preprocessing, obtains remote sensing image and initial segmentation;Remote sensing image and initial segmentation are input into trained remote sensing image segmentation error correction model: carry out feature extraction by IS feature extraction module, obtain multi-scale high-level semantic feature and initial segmentation feature;Through channel space attention module, error estimation is carried out to multi-scale high-level semantic feature and initial segmentation feature, to obtain error estimation feature and error estimation result;Through error notification refinement module, error correction is carried out to initial segmentation feature, error estimation feature and error estimation result, to obtain segmentation repair result.The application can effectively improve the segmentation accuracy of remote sensing image, and shows strong adaptability in complex background and high detail requirement task.