Semi-supervised remote sensing image target detection and segmentation method based on class activation graph

Abstract

The invention provides a semi-supervised remote sensing image target detection and segmentation method based on a class activation graph. The method comprises the following steps of firstly, generating a classification annotation data set by utilizing given remote sensing image annotation data, training a global average pooling (GAP) classification convolutional neural network model, and constructing a convolutional neural network model capable of generating a class activation map (CAM) by utilizing a weight superposition principle of a feature map; secondly, performing semi-supervised training on the target detection and segmentation model by taking the class activation graph and the real label as training targets through data enhancement; then, verifying the target detection and segmentation model by using a test set with a real label to obtain a model with higher detection and segmentation precision. and finally, under the condition that only a small amount of annotation data is used for training, the method has good remote sensing image target detection and segmentation effects.

Description

technical field [0001] The invention belongs to the technical field of image target detection and segmentation, in particular to a semi-supervised remote sensing image target detection and segmentation method based on a class activation map. Background technique [0002] Convolutional neural networks (CNNs), as a model architecture for deep learning, have become the most effective method in the field of image processing and computer vision. The two characteristics of weight sharing and local receptive field reduce the number of weights, which reduces the computational complexity of the model; the translation invariance of image features also makes it have good feature extraction ability and high stability. [0003] At present, a large number of studies have used convolutional neural networks to explore target detection and image segmentation methods. Since R.Girshick et al. proposed the R-CNN deep learning model based on candidate regions in 2014, a number of classic target ...

AI Technical Summary

Problems solved by technology

[0005] For target detection and segmentation of remote sensing images, due to the large amount of data, the labor cost of data labeling has also increased significantly. Existing datasets usually only have relatively easy labeling of target frames, while pixel-level segmentation labeling is rare. less

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