An active learning method of hyperspectral image based on graph signal sampling

Abstract

The invention discloses a hyperspectral image active learning method based on graph signal sampling. The method comprises the following steps: after reading three-dimensional hyperspectral image data,rearranging, taking its category label as graph signal, using the hyperspectral image data to construct a weight matrix, and characterizing the connection relationship between graph signal points; preserving 8 nearest neighbor connections and sparse weight matrices; calculating a degree matrix, a normalized weight matrix, a normalized graph Laplace matrix, a second-order graph Laplace matrix; acquiring an initial training sample as an initial sampling point of a graph signal; using the graph sampling method, the weakest pixel points are connected in the non-sampling set of the graph signal; adding the sampling pixel points into the sampling set of the graph signal; judging whether the sampling pixel points belong to a test set, if the sampling pixel points belong to the test set, giving an expert tag and adding a training set to remove the test set; classification accuracy being verified by using graph reconstruction classification method; whether the number of training samples reaches the set value or not being judged; if the training samples do not reach the set value, the active learning process being withdrawn.

Description

technical field [0001] The invention relates to the technical field of high-dimensional image processing, in particular to an active learning method for hyperspectral images based on image signal sampling. Background technique [0002] Hyperspectral images are acquired by sensors with high spectral resolution, and usually hundreds of bands are used to image ground objects at the same time. Compared with other remote sensing images, hyperspectral images have the following characteristics: large amount of data, many spectral bands, high similarity between adjacent bands, and high data redundancy. The spectral resolution of hyperspectral images can reach the nanometer level, and the hyperspectral images of "map-spectrum integration" contain rich spatial information and spectral information. The developed hyperspectral image classification technology can use spatial information and spectral features to realize ground object Fine classification and identification. In addition, ...

AI Technical Summary

Problems solved by technology

Therefore, the improvement effect of this type of active learning method on hyperspectral image classification and the convergence speed of classification accuracy with the increase in the number of labeled samples need to be improved.

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