204 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 relates to the technical field of remote sensing image segmentation, and discloses a remote sensing image segmentation method based on a foreground sensing network, and the method comprises the steps: constructing the foreground sensing network based on an encoder and decoder structure, and constructing a combined loss function; training the foreground sensing network; segmenting by using the trained foreground sensing network to obtain a category probability graph of each pixel; and based on the dynamic multi-resolution attention, performing multi-scale grading on the category probability graph in combination with the environmental characteristics, dynamically adapting a post-processing strategy, and performing post-processing according to the adapted post-processing strategy to optimize a segmentation result. According to the method, an end-to-end intelligent segmentation framework is constructed through multi-scale feature fusion, environment adaptive post-processing and geographical semantic constraint, the segmentation precision, robustness and practicability in a complex remote sensing scene are remarkably improved, and an efficient solution is provided for the fields of natural resource management, disaster emergency response and the like.

The invention discloses a remote sensing image segmentation method and system based on a high-similarity transmission attention mechanism. The method comprises the steps that a high-similarity transmission attention feature extraction network is constructed, multi-scale local features are extracted through preprocessing and a local exploration block, details are reserved in combination with jump connection, and high-similarity features are screened through an attention module; the features are fused with original features to obtain enhanced features; performing multi-scale up-sampling on the enhanced features to recover spatial information, optimizing details by combining coding features, and realizing model training by combining cross entropy and Dice loss; and inputting a target remote sensing image needing to be processed into the optimized model, outputting a segmentation mask image with the same size as the target input remote sensing image, and marking regions of different surface feature categories. According to the invention, the segmentation precision in a complex scene can be obviously improved; the generalization ability of the model is enhanced; the method provides powerful technical support for automatic processing of remote sensing images, and is suitable for practical applications such as urban planning and environment monitoring.

The invention discloses a remote sensing image segmentation method based on global feature enhancement and Fourier detail adjustment, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: constructing an image segmentation model comprising a wavelet-Mama global feature enhancement module, a fast Fourier detail adjustment unit and a decoding and segmentation prediction module; performing remote sensing image segmentation training on the built image segmentation model; and performing image segmentation on the target remote sensing image by using the trained image segmentation model. According to the invention, through the wavelet-Mama global feature enhancement module and the fast Fourier detail adjustment unit, the expression ability of surface feature structures, textures and edge information in remote sensing images can be effectively improved, and high-precision segmentation of small targets and fuzzy boundaries in complex scenes is realized. The method is especially suitable for accurate recognition of buildings, roads, water bodies and other targets under high-resolution remote sensing images, and has high practical value and popularization prospects.

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 belongs to the field of remote sensing image segmentation, and provides a double-branch remote sensing image semantic segmentation method and system, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and obtaining a preprocessed remote sensing image; based on the preprocessed remote sensing image, performing semantic segmentation by using a pre-trained double-branch remote sensing image semantic segmentation model to obtain a segmentation result, specifically, performing four times of convolution operation on the preprocessed remote sensing image by using a main branch to obtain main coding features of different scales; on the basis of the preprocessed remote sensing image, four times of global feature extraction are carried out in sequence by using an auxiliary branch, and then space-channel two-dimensional cooperative modulation is carried out on the global features of four levels to obtain auxiliary coding features of different scales; fusing the auxiliary coding features of different scales with the main coding features of corresponding scales to obtain corresponding fused coding features of different scales; and decoding the fused coding features of different scales to obtain a segmentation result.

The invention discloses a remote sensing image segmentation method and system based on convolution-state space fusion and a position trigger, and the method comprises the steps: carrying out the preprocessing, Patch embedding and spatial position coding of an original remote sensing image, converting a large-size image into a unified-scale structured representation, and maintaining the spatial position information; a patch vector sequence with position codes is decoupled through channels of a plurality of convolution-state space fusion units, convolution branches are respectively utilized to extract fine-grained edge and local structure features, state space modeling branches are utilized to capture long-range context information, multi-scale feature fusion is carried out through a cross-modal attention mechanism, and a multi-scale feature fusion result is obtained. A position trigger is introduced for multiple times to dynamically reinforce an edge and a small target response area, and fusion features are output; a classification probability graph is mapped through adaptive pooling and a full connection layer, and a supervision loss function is constructed to improve the segmentation performance; according to the method, the small target and boundary segmentation precision is remarkably improved, and the calculation efficiency and robustness are considered.

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 based on cross-normal-form feature fusion and alignment, and the method comprises the steps: carrying out the preprocessing of an input remote sensing image, and extracting an initial feature; inputting a cross-normal-form feature fusion and alignment network, and fusing multi-modal and cross-scale remote sensing image structure information through sparse channel enhancement and space alignment and space pixel refining and channel alignment to obtain a first-stage fusion feature; inputting a multi-stage cross-paradigm enhanced feature extraction network, fusing local details and global context information through multi-level information interaction and a dynamic gating mechanism, and gradually extracting a joint feature map of semantic and spatial structure collaborative expression; a final semantic segmentation result is generated through the segmentation head, and composite loss is calculated based on a real label; according to the method, the multi-stage feature extraction network and a cross-normal-form feature alignment mechanism are constructed, local textures, spatial contexts and multi-modal information are effectively fused, and the segmentation performance is enhanced while the calculation efficiency is guaranteed.

The invention discloses a sea-land island remote sensing image segmentation method based on feature difference enhancement fusion, and the method comprises the steps: collecting sea-land island remote sensing image data, and constructing a sea-land island remote sensing image data set; constructing an initial sea-land island remote sensing image segmentation model; calculating a loss function value of the model, performing back propagation, and training to obtain a sea-land island remote sensing image segmentation model; and outputting a segmentation result graph based on the trained model. According to the method, a coding-decoding architecture is adopted, a difference attention fusion module is introduced in a coding stage, and the two features are fused based on complementarity of global and local features so as to enhance the expression ability of the model; a cross fusion module is introduced in the decoding stage, multi-scale feature fusion is performed on features from different coding stages, and the segmentation precision is improved; and finally, outputting a segmentation prediction result through a full connection layer, and realizing accurate training optimization in combination with a multi-term auxiliary loss function, thereby improving the efficiency and accuracy of sea-land island remote sensing image segmentation.

The invention discloses a remote sensing image segmentation method and system based on multi-scale context enhancement, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a to-be-processed remote sensing image, inputting the to-be-processed remote sensing image into a global-local fusion network, and obtaining a multi-scale feature; inputting the multi-scale feature into a cascade fusion network to obtain a first processing feature, a second processing feature, a third processing feature and an integration feature; inputting the integrated features into a multi-scale context fusion network to obtain multi-scale semantic features; fusing the first processing feature, the second processing feature and the third processing feature with the multi-scale semantic feature to correspondingly obtain a first fusion feature, a second fusion feature and a third fusion feature, and fusing the first fusion feature, the second fusion feature and the third fusion feature to obtain a cascade feature; and inputting to a result output network based on the cascade features to obtain a segmentation prediction result of the remote sensing image. And the segmentation precision of the complex ground feature boundary and the small target in the remote sensing image is effectively improved.

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 discloses a remote sensing image segmentation method based on dual-path multi-scale attention and boundary perception, belongs to the technical field of computer vision and deep learning, and solves the problem that in the existing network design, capture of multi-scale context information and maintenance of high-resolution spatial details cannot be effectively balanced. And the target boundary is accurately perceived and enhanced. Comprising the following steps: S1, acquiring a remote sensing image; s2, establishing a dual-path neural network architecture, wherein the dual-path neural network architecture comprises an HR path, an LR path and a boundary enhancement dual fusion path; and S3, inputting the remote sensing image into the dual-path neural network architecture to complete target segmentation in the remote sensing image.

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.

An SAR sea ice remote sensing image segmentation method based on an MAE and a ViT model comprises the following steps that an unmarked AI4Arctic data set is used, a mask auto-encoder MAE is used for pre-training a visual Transform model, a picture is segmented into patches, then random masking is carried out, sine and cosine position embedding is adopted for carrying out position coding on visible patches, and a pre-trained ViT model is obtained; three segmentation labels of sea ice concentration SIC, sea ice development stage SOD and floating ice size FLOE are extracted from AI4Arctic original NetCDF ice map data, a single-channel grey-scale map is converted into a three-channel PNG image to serve as a fine tuning data set, fine tuning training of an encoder-decoder architecture is carried out in an end-to-end mode, any weight is not frozen in training, and the fine tuning data set serves as a fine tuning data set. And a multi-task ViT prediction model taking SIC, SOD and FLOE as segmentation targets is obtained. The ViT of the invention can better capture SAR image features, improve the accuracy of sea ice segmentation, significantly reduce the demand for training computing power and dependence on data annotation, and improve the speed of model training.

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 present invention provides a cross-modal remote sensing image and text retrieval method based on multi-level semantic collaborative matching, including: extracting regions of interest using a semantic segmentation algorithm through an image preprocessing module, segmenting remote sensing images into multiple regions and generating image blocks; extracting global features, regional features, and pixel-level features of the image respectively, and performing fine-grained encoding on key areas such as fine-grained ground feature edges; a text multi-level encoding module performs feature encoding on the document, sentence, and word levels of the text based on a pre-trained language model to ensure multi-level understanding of the text; in a multi-level matching and fusion module, the similarity between the remote sensing image and the text description is calculated through a cross-attention mechanism, and weighted fusion of features at all levels is performed to finally output a retrieval score. This method not only improves the accuracy and robustness of image and text retrieval, but can also be widely used in remote sensing monitoring, environmental change identification, geographic information systems and other fields.

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 multilayer feature fusion and prior guidance, and relates to the field of remote sensing image segmentation, and the method comprises the following steps: carrying out the processing of a multistage preprocessing module, a convolution layer and a prior guidance feature aggregation-coordination module on a remote sensing image through a feature extraction network, and generating features F1, F2 and F3; and through an information fusion network, performing multi-stage prior guide feature aggregation-coordination module and convolutional layer processing on the features F1, F2 and F3, and generating a remote sensing image segmentation result P. According to the method, through multi-level feature fusion, prior guidance feature optimization and multi-level loss supervision, the segmentation precision and robustness of the remote sensing image in a complex scene are remarkably improved.

The invention discloses a remote sensing image segmentation method and system based on hierarchical multi-modal feature fusion, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a hyperspectral image and a synthetic aperture radar image of a to-be-segmented target; inputting the hyperspectral image into an HSI feature extraction network to obtain a first aggregation feature; inputting a synthetic aperture radar image into an SAR feature extraction network to obtain a second aggregation feature; inputting the first aggregation feature into a first weight acquisition network to obtain a first related weight; based on fusion of the second aggregation feature and the first correlation weight, a modal interaction feature is obtained; inputting the modal interaction features into a second weight acquisition network to obtain a second related weight; fusing based on the first aggregation feature and the second correlation weight to obtain a multi-modal remote sensing feature; and inputting the multi-modal remote sensing features into a classification output network to obtain a segmentation result of the to-be-segmented target. And the accuracy of remote sensing image segmentation is improved.

Invention Name: A method and system for edge-enhanced remote sensing image segmentation that integrates attention and spatial state models Abstract: The present application discloses a method and system for edge-enhanced remote sensing image segmentation that integrates attention and spatial state models. The implementation steps are: constructing an edge texture feature enhancement structure; introducing the edge texture enhancement structure into the SegNext semantic segmentation model; dividing the remote sensing image segmentation dataset to generate a training sample set, a verification sample set, and a test sample set; preprocessing the dataset; using a neural network to preliminarily extract fine features of the optical remote sensing image, and then training the model with an edge texture enhancement decoder of the channel attention and spatial state model; finally, sending the test sample data to the edge texture enhancement model of the trained attention and spatial state model to obtain the test results. The patent of this invention utilizes the constructed edge texture feature enhancement module and the SegNext semantic segmentation model for collaborative training, which enhances the edge texture features while ensuring the features of the ground objects, thereby improving the accuracy of segmentation.

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.