Projection pursuit hyperspectral image segmentation method based on transfer learning

Abstract

The invention discloses a projection pursuit hyperspectral image segmentation method based on transfer learning, belonging to the technical field of image processing. The technical key points are as follows: the multiband characteristic of the hyperspectral image data is utilized to regard each wave band as a gray level image, each image is analyzed and researched; the characteristic that the images of different wave bands are similar but not the same to introduce transfer learning in the projection pursuit clustering algorithm; and a ground object marked graph is used to obtain the label of the source domain image data, and the known label knowledge is utilized to guide the image data without label in the object domain and obtain the optimal projection direction and the optimal subspace, thus increasing the segmenting precision. The method has the advantage that the priori knowledge is utilized to increase the segmenting precision, and can be used for military reconnaissance and in the civil and industrial fields.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to hyperspectral image segmentation, and can be used in military reconnaissance means and civil and industrial fields. Background technique [0002] Hyperspectral remote sensing is one of the most important developments in the field of remote sensing since the 1980s. It has become a hot topic in the field of international remote sensing technology research in the 1990s, and it will also be the frontier technology of remote sensing in the next few decades. Hyperspectral remote sensing technology uses imaging spectrometers to image surface objects simultaneously with tens or hundreds of bands with nanoscale spectral resolution, and can obtain continuous spectral information of surface objects, realizing spatial information, radiation information, and spectral information of surface objects. The synchronous acquisition of , has the feature of "map integration". Hyperspectral images...

AI Technical Summary

Problems solved by technology

In the traditional hyperspectral image segmentation, the K-means clustering algorithm and other clustering algorithms are used, but the K-means clustering algorithm is used to directly segment a single image or a multi-band image, and its segmentation accuracy is often not high. The clustering algorithm directly segments a single image or a multi-band image. Although its segmentation accuracy is improved compared with the K-means clustering algorithm, it cannot make full use of the existing prior knowledge, and its segmentation accuracy is still not high.

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