Hyperspectral Image Recognition Based on Artificial Neural Network

Abstract

This paper aims to reduce the dimensions of the data in remote sensing hyperspectral image (HIS), and solve the information redundancy caused by the numerous bands in the image. To this end, the neural network sensitivity analysis (NNSA) was introduced to simplify the dimensionality reduction process. Meanwhile, the convolutional neural network (CNN) was adopted as the classification algorithm for the HIS, seeking to prevent the complex data reconstruction of feature extraction and classification. Then, the proposed method was contrasted with several other classification methods in several experiments. The results show that the proposed method outperformed the contrast plans in classification accuracy. Thus, the artificial neural network (ANN) is good at reducing the dimensions of remote sensing HSI and the CNN is a reliable classification tool. The research findings shed new light on remote sensing image processing and other related operations.