Max-min distance embedding for unsupervised hyperspectral image classification in the satellite Internet of Things system.

In modern society, hyperspectral images have widely been used in many applications. In the earth observation system, hyperspectral images are acquired by hyperspectral sensors and transmitted to the cloud computing center for analysis, which are the part of satellite Internet of Things(IoT) to build a large scale land-cover surveillance system. Thus, during recent years, researchers throughout academia and industry have been advancing the classification of hyperspectral images. Luckily for swamped remote sensing researchers, new technology is allowing brands to extract useful knowledge using artificial intelligence to enhance supervised hyperspectral image classification technology. However, conventional methods that label and process hyperspectral images manually are labor and time-consuming. Hence, in this paper, we proposed an unsupervised hyperspectral image classification framework. The paper differs from the existing methods by three aspects: (1) We adopt multi-scale spatial features to enhance the discriminability of data. (2) We propose deep autoencoder with max-min distance embedding to extract features and reduce dimensions of data, which is more suitable for large-scale data clustering. (3) We apply k-means clustering method to get original classification, then optimize the results by the guided filter. Experimental results on two widely used hyperspectral data, Salinas and Pavia University, show the competitive performance obtained by the proposed framework compared with other approaches.