Land environment is one of the most commonly and importantly used synthetical natural environments in a virtual test. To recognize the ground truth for the construction of virtual land environment, a deep transfer hyperspectral image (HSI) classification method based on information measure and optimal neighborhood noise reduction was proposed in this article. Firstly, the information measure method was used to select the most valuable spectrum. Specifically, three representative bands were selected using the combination of entropy, color matching function, and mutual information. Based on the selected bands, a patch containing spatial-spectral information was constructed and used as the input of the convolutional neural networks (CNN) network. Then, in order to address the problem that a large number of labeled samples were required in deep learning method, the HSI classification method based on deep transfer learning was proposed. In the proposed method, the transfer of parameters ensured the classification performance with small training samples and reduced the training cost. Moreover, the optimal neighborhood noise reduction was used as the post-processing method to effectively eliminate the salt-and-pepper noise and further improve the classification performance. Experiments on two datasets demonstrated that the proposed method in this article had higher classification accuracy than similar methods.

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.

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