基于深度卷积神经网络的高光谱遥感图像分类

传统的高瀑布图像分类模型只考虑光谱特征信息，忽略了图像空间结构信息在分类中的重要作用。为提高高光谱遥感图像的分类精度，提出一种同时利用高瀑布图像的光谱信息和空间信息的深度卷积神经网络分类模型。通过对低层特征自动分层地学习来提取更加抽象的高层特征，提取的特征具有平移、缩放及其他形式扭曲等高度不变性；基于学习到的深度特征，用logistic回归分类器进行分类训练。高光谱数据实验结果表明，深度卷积神经网络模型能够提高高光谱遥感图像的分类精度，从而验证了深度卷积神经网络进行高瀑布图像分类的可行性和有效性。

Hyperspectral Remote Sensing Images Classification Using a Deep Convolutional Neural Network Model

The traditional hyperspectral image classification model only considers the spectral feature information, and ignores the important role of image spatial structure information in classification. In order to improve the classification accuracy of hyperspectral remote sensing image, this paper present a deep learning model utilizing the rich spectral and spatial information in hyperspectral images for land cover classification application. The proposed model is able to automatically extract more abstract high-level features from the low-level features for classification. In addition, the network structure is highly invariant to translation, scaling and other forms of distortion. Experiment results show that the deep learning method can provide high performances in hyperspectral image classification applications.The feasibility and effectiveness of the deep convolution neural network for classification of hyperspectral images are verified.