High-spectrum image classification method based on combined loss enhanced network

Abstract

The invention discloses a high-spectrum image classification method based on a combined loss enhanced network, and the method is characterized in that the method comprises the following steps: 1), PCA dimension reduction; 2), spatial domain block extraction; 3), coding path feature extraction; 4), classification task training target building; 5), decoding path feature extraction and reconstruction; 6), network combined training; 7), high-spectral testing classification. The method can achieve the combination of learning reconstruction loss and classification discrimination loss functions in the same network structure in a mode of end-to-end training, so the spatial spectrum information of a high-spectral image is used efficiently, and the learning of CNN for unimportant feature variables is automatically weakened, so as to reduce the complexity of a high-spectral classification model. Meanwhile, the dependence of a high-spectrum image classification method on a label sample is reduced, and the classification precision is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent image processing, in particular to a hyperspectral image classification method based on a joint loss enhancement network. Background technique [0002] Hyperspectral image (HSI for short) has the unique advantages of high spectral resolution and map-spectrum integration, and has great application prospects in the fields of target tracking, environmental protection, agricultural monitoring, and weather forecasting. Classifying each pixel in a hyperspectral image is an important part of hyperspectral remote sensing applications and has great research significance. [0003] Traditional hyperspectral image classification methods often only use artificially extracted features in low-dimensional space. Typical methods mainly include: K-means clustering (K-means) method, logistic regression (Logistic regression, LR), support vector machine ( Support vector machine, SVM), etc., however, these classif...

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Problems solved by technology

[0003] Traditional hyperspectral image classification methods often only use artificially extracted features in low-dimensional space. Typical methods mainly include: K-means clustering (K-means) method, logistic regression (Logistic regression, LR), support vector machine ( Support vector machine, SVM), etc., however, these classificat...

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