一种基于DBN的高光谱遥感图像分类方法

高光谱遥感数据越来越普及并为人们广泛使用，基于高光谱数据的地面物体精确分类是高光谱遥感技术的核心应用之一。对高光谱数据进行提特征提取是进行地物分类的有效方法。深度学习是机器学习研究中的新领域，它多隐层的多层感知器结构使其能够学习到对数据有更本质的刻画的特征，在图像分类和可视化领域取得了更好的成绩。深度置信网（deep belief network ，DBN）是深度学习网络中常见的模型。利用高光谱数据的高维特性，搭建基于DBN的高光谱图像分类模型，结合高光谱数据的空间结构对地物进行分类。实验表明，基于DBN的高光谱图像分类方法可以得到更好的分类效果。

Deep neural networks based on hyperspectral image classification

Hyperspectral data is becoming increasingly popular and widely used .Accurate classification of the high ground objects is one of the core application of hyperspectral remote sensing technology . Extracting feature from hyperspectral data is an effective method for classification .Deep learning is the new areas of machine learning research . It has multilayer perceptron structure so that it can learn to portray a more essential characteristic ,and have better results in the field of image classification and visualization .DBN is a normal model of deep learning network . A hyperspectral image classification model based on DBN is constructed by using high dimensional feature of hyperspectral data and combining the spatial structure of hyperspectral data .Experiment shows that high spectral image classification based on DBN can get better classification results .