Hyperspectral image classification method based on multi-scale spectral space convolutional neural network

Abstract

The invention discloses a hyperspectral image classification method of a multi-scale spectral space convolutional neural network, and mainly solves the problems that in the prior art, only single-scale features are extracted during inter-spectral feature extraction and spatial feature extraction, and the classification effect on ground object categories with inconcentrated sample distribution or small sample size is poor. According to the implementation scheme, the method comprises the following steps: 1) inputting a hyperspectral image to generate a training sample set and a test sample set with different sample numbers; 2) constructing a multi-scale spectral space convolutional neural network; 3) inputting the training set into a multi-scale spectral space convolutional neural network toobtain a prediction category, calculating hinge cross entropy loss by using the prediction category and a real label, and training the network by using a stochastic gradient descent method until thehinge cross entropy loss converges; and 4) inputting the test sample into the trained multi-scale spectral space convolutional neural network to obtain a classification result. The method can obtain high-accuracy classification under the condition of few training samples, and can be used for ground object type detection of a hyperspectral image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing information processing, and further relates to a hyperspectral image classification method, which can be used to detect the types of ground objects in hyperspectral images. Background technique [0002] Hyperspectral records the continuous spectral characteristics of ground objects with its rich band information, and has the possibility of recognizing more types of ground objects and classifying objects with higher accuracy. The key to hyperspectral image classification technology is to use the spatial and spectral features of hyperspectral images to classify sample categories. It is of great significance in land resource assessment and disaster monitoring. However, the existing classification methods still mainly rely on a large number of training samples. Since sample labels are difficult to obtain, it is easy to cause overfitting problems in the case of few samples, which in turn affect...

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This model can solve the problem of hyper...

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