265 results about "Remote sensing image segmentation" patented technology

The invention provides a cross-modal remote sensing image-text retrieval method based on multistage semantic collaborative matching, which comprises the following steps of: extracting a region of interest by using a semantic segmentation algorithm through an image preprocessing module, segmenting a remote sensing image into a plurality of regions and generating image blocks; respectively extracting global features, regional features and pixel-level features of the image, and carrying out fine-grained coding on key regions such as fine-grained ground feature edges and the like; the text multi-level coding module is used for carrying out three-level feature coding of documents, sentences and words on texts based on a pre-training language model to ensure multi-level understanding of the texts; in the multi-level matching and fusion module, the similarity between the remote sensing image and the text description is calculated through a cross attention mechanism, weighted fusion is carried out on features of all levels, and finally a retrieval score is output. The method not only improves the accuracy and robustness of image-text retrieval, but also can be widely applied to the fields of remote sensing monitoring, environment change recognition, geographic information systems and the like.

The invention relates to a hybrid structure remote sensing image segmentation method based on a state space model, and the method comprises the steps: obtaining a to-be-processed remote sensing image, inputting the to-be-processed remote sensing image into a hybrid structure remote sensing image segmentation model, and obtaining a segmentation result; the mixed structure remote sensing image segmentation model is obtained by training a training set, and the training set comprises a remote sensing image; according to the hybrid structure remote sensing image segmentation model, a residual network sub-model based on a convolutional neural network and an improved Mama sub-model are utilized to establish a dual-encoder structure for extracting multi-scale features, and a Transform sub-model based on a hybrid attention convolution module is utilized to establish a decoder for capturing a feature dependency relationship of a global range through the multi-scale features and simultaneously extracting the feature dependency relationship of the global range. Extracting local features and capturing spatial information to generate a segmentation result; wherein the improved Mama sub-model is an original Mama sub-model into which a two-dimensional selective scanning module and a CSAM attention fusion module are introduced.

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 belongs to the technical field of remote sensing image segmentation, and particularly relates to a method for generating an adversarial frequency perception image segmentation network, which comprises the following steps of: 1, acquiring a public remote sensing image data set; 2, inputting the remote sensing image data set into a self-adaptive frequency driving feature enhancement module, and decoupling high-frequency features and low-frequency features; the frequency perception adversarial feature enhancement generates more real high and low frequency features through a generative adversarial network, and the resolution capability of the model is improved; and the CLIP text encoder converts the text description into semantic feature vectors to provide support for subsequent cross-modal matching. According to the remote sensing image semantic segmentation method, innovative design is carried out in the aspects of multi-modal fusion, high and low frequency information extraction, adversarial training and the like for remote sensing image semantic segmentation tasks with complex modals, and the segmentation precision, generalization ability and robustness are effectively improved.

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 relates to the technical field of image processing, in particular to a remote sensing image segmentation method based on multilayer wavelet transform and a dynamic memory network, which comprises the following steps: decomposing an image into a low-frequency sub-band and a plurality of high-frequency sub-bands through multilayer Haar wavelet transform, and performing feature extraction on each frequency band in combination with convolution operation to obtain a final feature map; constructing a dynamic memory unit network, performing dynamic feature extraction on the feature map by adopting a query-key-value structure, generating a dynamic memory enhanced feature with global context information, performing global-local feature modeling on the feature map, and fusing the dynamic memory enhanced feature and the global-local feature to obtain a fused feature map; and segmenting the fused feature map to obtain a final segmentation result. According to the method, low-frequency smooth information and high-frequency detail information of the image can be accurately extracted, efficient fusion of global and local features can be realized, and the accuracy and robustness of remote sensing image segmentation are improved.

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 restoration method and device based on uncertainty and a storage medium, and belongs to the technical field of remote sensing image processing. The method comprises the steps of preprocessing an acquired remote sensing image, generating the remote sensing image and an initial segmentation mask, and inputting the remote sensing image and the initial segmentation mask into a trained remote sensing image segmentation restoration model; the method comprises the following steps: firstly, performing feature extraction and coding on a remote sensing image and an initial segmentation mask through a feature extraction and coding module to generate a multi-scale advanced semantic feature and an initial segmentation feature; then performing feature enhancement, mapping, uncertainty calculation and weighting through an uncertainty error estimation module to respectively obtain an error estimation feature, an error estimation result, total uncertainty and an uncertainty weighting result; and finally, fusion and correction are carried out through an uncertainty guide restoration module, and a segmentation restoration result is obtained. According to the method, the restoration weight can be dynamically adjusted by calculating and quantifying the uncertainty, and the precision and stability of remote sensing image segmentation are improved.

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 relates to a remote sensing image segmentation method based on a visual basic model SAM and double edge correction. The remote sensing image segmentation method comprises the following steps: acquiring a remote sensing image and preprocessing the remote sensing image to generate a preprocessed image; inputting the preprocessed image into an SAM model for processing, and generating an initial ground feature mask; performing edge detection on the preprocessed image according to the initial ground feature mask to generate an initial edge intensity graph; carrying out optimization processing on the initial edge strength graph to obtain an optimized edge strength graph; dividing the preprocessed image, generating a plurality of overlapped sub-images, inputting the overlapped sub-images into an SAM model for processing, and generating a binary mask; performing segmentation analysis processing on the optimized boundary diagram and the binary mask to generate a complete segmentation result; according to the method, the zero sample learning capability of the SAM model is utilized, intelligent segmentation is realized under the condition of no labeled data, boundary information in the image is effectively extracted through edge detection, and the recognition capability of the system on a fuzzy or noise region is enhanced.

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 based on double-domain feature enhancement and adaptive scale fusion. The method comprises the following steps: S1, acquiring remote sensing image data; s2, constructing a feature extraction network comprising a double-domain feature enhancement module, and performing multi-scale feature extraction on the input remote sensing image to obtain feature maps of different levels; s3, constructing an adaptive scale fusion module, and performing feature fusion on the extracted multi-scale feature map to obtain a fused feature map; and S4, further processing based on the fused feature map to generate a final semantic segmentation result map. The invention also discloses a remote sensing image segmentation system based on double-domain feature enhancement and adaptive scale fusion. According to the method, the feature extraction network and the adaptive scale feature fusion network are linked, fusion of different semantic information feature maps is realized, and the detail analysis capability of complex remote sensing images is improved while high precision is ensured.

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 discloses a remote sensing image segmentation method combined with non-additional parameter similarity attention, which relates to the field of remote sensing image segmentation, and comprises the following steps of: extracting multi-scale features of a remote sensing image by utilizing a feature extraction and fusion optimization network, performing fusion optimization, and generating initial features; performing sensitivity enhancement through a similarity attention unit without additional parameters in the feature enhancement network, performing boundary and texture feature extraction through a boundary and texture feature extraction unit, and performing fusion to generate enhanced features; and in combination with the segmentation network, performing optimization and category prediction on the enhanced features, and outputting a segmentation result of the remote sensing image. According to the method, the model efficiency is remarkably improved on the premise of not increasing extra parameters, the problems of detail loss and boundary fracture in a complex scene are effectively solved, and the adaptability and segmentation quality of a low-contrast region and a high-noise remote sensing image are improved.

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 urban landscape classification method and system based on a self-attention network, and relates to the technical field of remote sensing image recognition and classification, and the method comprises the steps: obtaining a remote sensing image and a classification mark, and carrying out the data enhancement; segmenting the remote sensing image in the data set to obtain an image block sequence; constructing a self-attention network based on a visual Transform model, wherein the self-attention network comprises an embedded layer, an encoder and a task head; inputting an image block sequence and a classification mark to the self-attention network, obtaining an embedded representation in an embedding layer, then connecting the embedded representation with the classification mark, and obtaining a result embedded image block sequence with a mark; embedding the marked result into an image block sequence, inputting the image block sequence into an encoder, and generating a feature representation and an attention score; and inputting the feature representation into a task head, generating a classification label according to the attention score, and completing remote sensing image urban landscape classification. The method is high in generalization performance, classification is performed based on the global context dependency relationship of the image, and the accuracy is improved.

The invention discloses a remote sensing image segmentation method based on a multi-scale feature fusion and index selection mechanism, and belongs to the technical field of remote sensing image processing, and the method comprises the following steps: extracting different scale feature information in a remote sensing image through a multi-scale convolutional network, and obtaining a fusion feature map of the remote sensing image; performing feature aggregation on the fused feature map of the remote sensing image by using an index selection enhancement module based on a sparse attention mechanism to obtain an enhanced feature map; and decoding the enhanced feature map by using a decoder to obtain a remote sensing image segmentation result. By introducing the multi-scale feature extraction and sparse attention mechanism, the method can better focus on the important region in the remote sensing image, thereby improving the segmentation precision.

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 grassland pasturing area settlement identification and classification method and system based on a remote sensing image, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a current remote sensing image and a historical remote sensing image of a selected region, segmenting the region in each remote sensing image, screening the regions with the vegetation index values lower than a set threshold value, and carrying out the recognition and classification of grassland pasturing area settlement. The method comprises the following steps: marking a changing region in a current remote sensing image, forming a to-be-recognized region set together with all low vegetation index regions screened out from the current remote sensing image, inputting the region set with respective characteristics into a pre-trained classification model, and outputting a type corresponding to each region by the classification model. According to the method, the dynamic change characteristics of grassland pasturing area settlement can be captured, the area characteristic set is input into the pre-trained classification model, and the classification logic is designed in combination with the actual scene characteristics of the grassland pasturing area, so that the model can better adapt to the differences of different terrains, vegetation coverage and settlement distribution.

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 discloses a domain generalization method for a remote sensing image segmentation task, which relates to the technical field of image processing, and comprises the following steps of: firstly, extracting a feature map from a remote sensing image sample by utilizing a basic remote sensing image segmentation model, and decomposing the features into a domain specific component and a domain invariant component by adopting a feature decoupler; therefore, differences and generality among different domains can be captured more accurately. Thirdly, model parameters are updated based on the calculated cost loss, a meta-learning framework is used for optimizing a feature decoupler, and independence and semantic attributes of decoupling components are ensured; through the technical means of continuous value space modeling, vector orthogonal decoupling, generated data maintenance and the like, the remote sensing image segmentation accuracy is improved, and the generalization ability of the model in an unknown domain is remarkably enhanced.

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.