Abstract

In order to construct virtual land environment for virtual test, we propose a construction method of virtual land environment using multi-satellite remote sensing data, the key step of which is accurate recognition of ground object. In this paper, a method of ground object recognition based on hyperspectral image (HSI) was proposed, i.e., a HSI classification method based on information measure and convolutional neural networks (CNN) combined with spatial-spectral information. Firstly, the most important three spectra of the hyperspectral image was selected based on information measure. Specifically, the entropy and color-matching functions were applied to determine the candidate spectra sets from all the spectra of the hyperspectral image. Then three spectra with the largest amount of information were selected through the minimum mutual information. Through the above two steps, the dimensionality reduction for hyperspectral images was effectively achieved. Based on the three selected spectra, the CNN network input combined with the spatial-spectral information was designed. Two input strategies were designed: (1) The patch surrounding the pixel to be classified was directly intercepted from the grayscale images of the three selected spectra. (2) In order to highlight the effect of the spectrum of the pixel to be classified, all the spectral components of this pixel were superimposed on the patch obtained by the previous strategy. As a result, a new patch with more prominent spectral components of the pixel to be classified was obtained. Using the two public hyperspectral datasets, Salinas and Pavia Center, the experiments of on both parameter selection and classification performance were performed to verify that the proposed methods had better classification performance.

中文翻译：

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基于信息量测和CNN的空间光谱高光谱图像分类

摘要

为了构建用于虚拟测试的虚拟土地环境，提出了一种利用多卫星遥感数据构建虚拟土地环境的方法，其关键步骤是对地面物体的准确识别。提出了一种基于高光谱图像（HSI）的地物识别方法，即基于信息量度和卷积神经网络（CNN）结合空间光谱信息的HSI分类方法。首先，基于信息量度，选择了高光谱图像中最重要的三个光谱。具体而言，应用熵和颜色匹配函数从高光谱图像的所有光谱中确定候选光谱集。然后，通过最少的互信息选择信息量最大的三个光谱。通过以上两个步骤，有效地实现了高光谱图像的降维。基于所选的三个光谱，设计了CNN网络输入并结合了空间光谱信息。设计了两种输入策略：（1）直接从三个选定光谱的灰度图像中截取要分类像素周围的色块。（2）为了突出要分类的像素的光谱效果，将该像素的所有光谱分量叠加在通过先前策略获得的贴片上。结果，获得了具有待分类像素的光谱成分更加突出的新斑块。使用两个公共高光谱数据集，