A time-varying feature extraction method for hyperspectral images based on low-rank decomposition and spatial spectral constraints

Abstract

The invention relates to a low-rank decomposition and space spectrum constraint-based hyperspectral image time domain change feature extraction method. The method mainly comprises the following stepsof: calculating a difference image between two hyperspectral remote sensing images acquired at a same place and at different times; establishing a low-rank matrix decomposition model with a space spectrum constraint by utilizing internal data structural characteristics of the difference image; and solving each component of the model through an alternate iteration manner so as to extract a time domain change feature. According to the method, the low-rank matrix decomposition model with the space spectrum constraint and a solution algorithm of the model are disclosed by sufficiently utilizing internal structures of data, so that the time domain change feature is effectively extracted, multiple forms of noises are removed, real changes are strengthened and false changes caused by noises are suppressed, thereby improving the change detection precision of time domain change hyperspectral remote sensing images.

Description

technical field [0001] The invention relates to a method for extracting time-varying features of hyperspectral images based on low-rank decomposition and space spectrum constraints. Background technique [0002] Remote sensing technology is a new comprehensive technology developed in the 1960s. It is closely related to science and technology such as space, electron optics, computer, and geography. It is a powerful technical means for studying the earth's resources and environment. Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology with spectral technology. The hyperspectral imager simultaneously detects the two-dimensional geometric space and one-dimensional spectral information of the target on dozens to hundreds of narrow and continuous bands of the electromagnetic spectrum, providing extremely rich data for the extraction and analysis of ground object information, thus It is widely used in geological sc...

AI Technical Summary

Problems solved by technology

However, there are still some deficiencies in the existing research results: First, the ubiquitous and diverse noises in time-varying hyperspectral images are ignored, including outliers and thermal noise caused by registration errors, spectral deviations, hardware limitations, etc. , so that the "false changes" caused by noise cannot be effectively suppressed; second, only the spectral information of the hyperspectral image is considered, and its spatial characteristics or spatial spectral characteristics are not fully utilized

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