4results about How to "Improve Semantic Segmentation" patented technology

This invention relates to the field of image processing technology and discloses a semi-supervised semantic segmentation method, apparatus, device, and medium for remote sensing images. The method includes: acquiring several remote sensing images and a panchromatic image corresponding to each remote sensing image; employing various panchromatic sharpening techniques to fuse the spectral information in each remote sensing image with the high spatial resolution information in the corresponding panchromatic image to obtain a high-resolution multispectral image; fusing the RGB band information in each high-resolution multispectral image with the remaining band information to obtain a band-fused image; during semantic segmentation model training, the supervised learning part uses weak perturbation techniques to perturb the labeled image, while the unsupervised learning part uses the high-resolution multispectral image and the band-fused image as multimodal fusion perturbations, combining weak perturbation techniques, strong perturbation techniques, and multimodal fusion perturbations to perturb the unlabeled image, so as to perform semantic segmentation through the trained semantic segmentation model.

The application discloses a point supervision semantic segmentation model construction and segmentation method based on SAM and CNN feature fusion, comprising the following steps: step one, acquiring remote sensing image data as a training set, and performing point labeling on target ground objects in the remote sensing image data to obtain a point label set, wherein the point label set is the category and position of the target ground objects; a point supervision semantic segmentation model is constructed; object structure information generated by using a convolutional neural network branch and a SAM driven fusion branch is used to realize collaborative modeling of image semantic information and structure information in combination with a cross-level fusion module CFM; meanwhile, under the condition of point supervision, the model can enhance the integrity control of the target region, improve the boundary positioning accuracy, and reduce the category confusion phenomenon, thereby significantly improving the effect of remote sensing image semantic segmentation. The technical problem that the existing point supervision semantic segmentation method is difficult to fully utilize image structure information and semantic information under the condition of sparse supervision information is solved.

The application belongs to the field of computer vision, and discloses a weakly supervised semantic segmentation method, system, device and medium based on random combination, which comprises the following steps: training classification networks N1, N2 and N3 respectively by using a training data set, a slice training data set and a slice training data set randomly combined, so that each network can extract different active regions in the picture, and the learning results of the other two networks are learned by using the mutual supervision training mode; finally, the prediction results of the three networks are combined to obtain the final semantic segmentation result, which is used as a semantic segmentation training data set to train a semantic segmentation model to predict the final semantic segmentation result. The application effectively utilizes the different perception areas of the network for the randomly combined slice pictures, and utilizes the different classification networks to perceive the categories of the same picture, thereby improving the semantic segmentation ability and prediction accuracy of the semantic segmentation model through the semantic segmentation data set obtained by combining the results of the three classification networks.

The invention discloses an image semantic segmentation method for separating attention based on a space channel. The method comprises the following steps: preprocessing an input image and extracting multi-scale features by an encoder; constructing a visual potential source item and solving a Poisson equation to obtain a potential field and a potential gradient; generating a space weight graph by utilizing the potential field, and forming a boundary enhancement weight graph by combining the potential gradient to realize space gating; channel characteristics are regarded as parallel capacitor layers to generate channel coupling weights for channel gating; fusing space and channel enhancement features; and a semantic segmentation result is output through step-by-step decoding. According to the method, by introducing a visual potential propagation and flux conservation mechanism, energy balance and accurate boundary control are realized in space and channel modeling, and the structural integrity and stability of a semantic segmentation result are improved.