A Classification Method for Hyperspectral Imagery Based on Deep Learning

Abstract

遥感高光谱成像能够获得丰富的地物光谱信息，这使得在传统的宽波段遥感中不可分辨的物质，在高光谱遥感中可以被分辨出来。高光谱图像具有“图谱合一”的特点，充分的利用高光谱图像中的光谱信息和空间信息是获得精确分类结果的前提。深度学习模型中的自编码神经网络能够实现高维数据的非线性降维，而卷积神经网络(Convolutional Neural Network, CNN)则能够自动的从图像中提取空间特征，基于此，本文提出了一种基于深度学习的Autoencoder-CNN高光谱图像分类方法。首先利用自编码神经网络对高光谱数据进行光谱维的降维，然后将卷积神经网络作为分类器，将待分类像元及其邻域像元一同作为卷积神经网络的输入，实现高光谱图像的空谱联合分类。 Remote sensing hyperspectral imaging can obtain abundant spectral information, which provides the possibility for the analysis of high precision terrain. The hyperspectral image has the characteristics of “map in one”, and the full use of spectral information and spatial information in hy- perspectral image is the premise of obtaining accurate classification results. Deep learning stack machine model in automatic encoding (Stack Auto-Encoder SAE) can effectively extract data in nonlinear information, and convolutional neural network (Convolutional Neural Network, CNN) can automatically extract features from the image. Based on this, this paper presents a classification method of hyperspectral images based on deep learning. Firstly, the spectral dimension of the hyperspectral data is reduced using automatic encoding machine, then convolutional neural network is used as the classifier, and the pixel and its neighborhood pixels are classified together as the input of the classifier, so as to realize the hyperspectral image classification with spectral space.