9 results about "Sar image segmentation" patented technology

The present invention provides a SAR image segmentation model training method, an image segmentation method, an apparatus, and an equipment. The method comprises: obtaining a sample radar image and a segmentation label map of the sample radar image; performing edge enhancement processing on the sample radar image to determine a sample edge intensity map; performing superpixel segmentation according to the sample edge intensity map to obtain a sample superpixel region image; using an initial convolutional neural network to perform feature extraction and semantic segmentation on the sample radar image to determine a sample category response map of the sample radar image; creating a graph structure according to the sample category response map and the sample superpixel region image, and using an initial graph convolutional network to perform semantic reasoning to obtain a sample segmented image; and training the initial convolutional neural network and the initial graph convolutional network according to the sample segmented image and the segmentation label map to obtain a SAR image segmentation model including a target convolutional neural network and a target graph convolutional network, so as to effectively improve segmentation accuracy and boundary clarity.

The present invention proposes a SAR image multi-feature region merging method based on SLIC superpixel, which is used to improve the segmentation accuracy of SAR images. The implementation steps are: using the SLIC superpixel algorithm to segment the SAR image; extracting the grayscale features and texture features of each superpixel; obtaining the similarity coefficient between every two adjacent superpixels; determining the superpixel pairs to be merged; calculating the merging index of each superpixel pair to be merged; obtaining the SAR image multi-feature region merging result; judging the number of superpixels and the size of the threshold; outputting the SAR image of the newly merged superpixel. The present invention first uses the SLIC superpixel algorithm to over-segment the SAR image, and then fuses the adjacent superpixels with the help of different features, thereby improving the segmentation accuracy of the SAR image.

The invention provides an SAR (Synthetic Aperture Radar) image segmentation model training method, an image segmentation method, a device and equipment. The method comprises the following steps: acquiring a sample radar image and a segmentation label graph of the sample radar image; performing edge enhancement processing on the sample radar image to determine a sample edge intensity graph; performing super-pixel segmentation according to the sample edge intensity image to obtain a sample super-pixel region image; performing feature extraction and semantic segmentation on the sample radar image by adopting an initial convolutional neural network, and determining a sample category response graph of the sample radar image; performing graph structure creation according to the sample category response graph and the sample superpixel region image, and performing semantic reasoning by adopting an initial graph convolutional network to obtain a sample segmentation image; and according to the sample segmentation image and the segmentation label image, training the initial convolutional neural network and the initial image convolutional network to obtain an SAR image segmentation model comprising a target convolutional neural network and a target image convolutional network so as to effectively improve the segmentation precision and the boundary definition.

The application provides a water body boundary identification method and device based on boundary perception collaborative optimization, belongs to the field of artificial intelligence remote sensing SAR image segmentation, and comprises the following steps: a deep learning model is constructed, a real-time double-branch semantic segmentation framework is adopted to meet the timeliness requirement of water body boundary identification; an auxiliary boundary prediction branch is introduced to highlight high-frequency semantic information, a boundary detection is taken as an optimization target, and a boundary perception loss is introduced to predict complex water body boundaries; a pixel attention module, a context fast aggregation module and a boundary attention guiding module are proposed to mine spatial detail information, context information and boundary information of a target image respectively, control effective learning of context semantic information, guarantee reliability and timeliness of extracted information, guide effective fusion of various information at a boundary area, jointly optimize original double-branch and auxiliary branches, and realize accurate identification of water body boundaries. The application can improve identification precision.

The invention provides a lightweight SAR image segmentation network system, a segmentation method, a device and a medium, and relates to the technical field of image processing, the system comprises a plurality of cascaded down-sampling modules configured to perform multiple times of down-sampling on an SAR image to obtain first feature maps of different scales; the cavity space pyramid pooling module is configured to perform cavity convolution on the first feature map output by the last stage of down-sampling and perform pooling on different scales to obtain a second feature map; the feature fusion module is configured to perform adaptive weighted fusion on the first feature map and the second feature map to obtain a third feature map; the lightweight channel attention module is configured to adaptively adjust the weight of each channel in the third feature map based on a lightweight channel attention mechanism to obtain a fourth feature map; and the up-sampling module is used for carrying out up-sampling on the fourth feature map to obtain a segmented image, and the segmented image comprises a segmentation result of the target object.

The invention discloses a multi-mode remote sensing image semantic segmentation method and device combining an optical image and an SAR (Synthetic Aperture Radar). The method comprises the following steps: inputting an optical image and an SAR image into a multi-mode remote sensing image semantic segmentation network to obtain an image segmentation result; the processing process of the multi-mode remote sensing image semantic segmentation network comprises the following steps: inputting an optical image into an optical branch to obtain an optical image segmentation result, and inputting an SAR image into an SAR branch to obtain an SAR image segmentation result; the features corresponding to the optical images output by the optical branches and the features corresponding to the SAR images output by the SAR branches are fused to obtain optical-SAR fusion features, and the optical-SAR fusion features are input into the fusion feature branches to obtain a fusion semantic segmentation result. According to the method, the ability of the model to express semantic and detail features by using optical-SAR cross-modal features is significantly improved, and a more efficient solution is provided for improvement of prediction accuracy of a remote sensing image semantic segmentation method.

A fast multi-scale superpixel segmentation method for synthetic aperture radar images includes the following steps: combining backscattering grayscale similarity, edge information similarity and homogeneity similarity to measure the final similarity of two pixels; performing a graph structure representation on the SAR image; constructing a minimum spanning tree for the SAR image; realizing fast multi-scale superpixel segmentation of the SAR image based on the minimum spanning tree, obtaining a superpixel segmentation boundary based on the segmentation result and generating a SAR image segmentation result. The present invention can instantly generate any number of superpixels with a tree structure, so that superpixel results of different levels can be segmented. The multi-scale superpixel segmentation method of the present invention can not only well maintain the image boundary, but also has a good ability to maintain local image details, and can generate segmentation results of any scale for SAR images in real time, which is helpful for obtaining information of targets of different sizes, and the execution efficiency is significantly improved.

The present invention discloses a real-time SAR image segmentation method based on Lovasz loss and lightweight bilateral network, which pre-trains and fine-tunes the BiSeNet network in sequence; wherein, during the pre-training process, the loss function of the BiSeNet network adopts the cross-entropy loss function, and during the fine-tuning training process, the loss function of the BiSeNet network adopts a joint loss function, and the joint loss function includes the Lovasz loss function and the cross-entropy loss function; the polarized SAR image is segmented based on the BiSeNet network after fine-tuning training; on the basis of the BiSeNet network, the present invention takes the commonly used index Intersection Over Union (IOU) in semantic segmentation as the optimization target, introduces the extended Lovasz loss for it, realizes real-time semantic segmentation of the lightweight bilateral network based on Lovasz loss, and obtains higher classification accuracy.

The present invention provides a SAR image segmentation method based on spatial domain statistical similarity and frequency domain texture similarity, comprising quantizing the SAR image, calculating an edge strength map, obtaining an initial segmentation result, forcibly merging small regions, calculating frequency domain texture similarity and a merging cost function, and displaying and comparing the detection results. The present invention solves the problem of high computational complexity in texture region merging in the prior art. The present invention uses a spatial domain to frequency domain transformation to simplify texture similarity, and combines a statistical similarity measure based on the Bhattacharyya distance and an edge penalty term to obtain a new merging cost function, thereby achieving a higher accuracy SAR image segmentation method and reducing computational complexity.