A Spectral Tensor Dimensionality Reduction and Classification Method Based on Tucker Decomposition

Abstract

The invention discloses a spectral tensor dimensionality reduction and classification method based on Tucker decomposition. In the method, the factors affecting the spectral characteristics of ground objects are regarded as intra-class factors, and the intra-class factors, class and pixel spectra are respectively used as a model to construct into a third-order tensor, and perform dimensionality reduction based on low-rank tensor decomposition; perform low-rank tensor decomposition on the third-order tensor to obtain the nuclear tensor-like space matrix U class , Intra-class factor space matrix U within‑class and the pixel spectral matrix U pixels ; classify the class-free test hyperspectral image d using a supervised classifier. The invention can classify hyperspectral images after the model is established without adjustment, while other tensor modeling methods need to repeatedly set and adjust parameters to achieve the best classification effect; the invention maps all pixel spectra of a class to the same coefficient vector, thereby minimizing the influence of various factors, not only improving the classification accuracy, but also stabilizing the results.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a spectral tensor dimension reduction and classification method based on Tucker decomposition. Background technique [0002] Hyperspectral images provide a detailed and rich description of the spectral characteristics of ground objects, greatly improving the classification ability of ground objects, and have been widely used in geological exploration and earth resource investigation, urban remote sensing and planning management, environmental and disaster monitoring, precision agriculture, surveying and mapping and archaeology. [0003] However, hyperspectral images are composed of a large number of band data, and these bands constitute a high-dimensional feature space, and its processing requires a huge amount of calculation, causing a "data disaster". To solve this problem, the most effective way is dimensionality reduction. Principal Compo...

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Problems solved by technology

However, this method only simply models the hyperspectral cube as a third-order tensor, without considering the real reason that affects the accuracy of hyperspectral classification: the spectral characteristics of ground objects are affected by various factors such as illumination, mixing, atmospheric scattering, and atmospheric radiation.

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