Multilinear ICA (independent component analysis)-based spectrum tensor dimension reduction classification method

Abstract

The invention discloses a multilinear ICA (independent component analysis)-based spectrum tensor dimension reduction classification method. In the method, factors affecting spectrum characteristics of a terrain are used as within-class factors, a within-class factor, a class and a pixel spectrum serve as a mode respectively to build a three-order tensor, and dimension reduction based on low rank tensor decomposition is carried out on the three-order tensor; the three-order tensor D is subjected to multilinear ICA decomposition, and a class space matrix Cclass and a within-class factor space matrix Cwithin-class are obtained; and a supervised classifier is adopted to classify classless test hyperspectral images d. After the model is built, the hyperspectral images can be classified, no adjustment is needed, and according to other tensor modeling methods, the best classification effects are achieved only by repeated setting and parameter adjustment. All pixel spectrums of the same class are mapped to the same coefficient vector, influences from various factors are reduced to the minimum, the classification precision is improved, and the result is stable. When unknown pixel spectrums are classified, which factor affects the spectrums can be inferred.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a classification method for spectral tensor dimension reduction based on multilinear ICA. Background technique [0002] Hyperspectral images provide a detailed and rich description of the spectral characteristics of ground objects, greatly improve the ability to classify ground objects, and have been widely used in geological exploration and earth resources survey, 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, which constitute a high-dimensional feature space, and their processing requires a huge amount of computation, causing a "data disaster". The most effective way to solve this problem is dimensionality reduction. Principal Component Analysis (PCA) is curr...

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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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