Hyperspectral and Infrared Image Collaborative Classification Based on Morphology Feature Extraction

With the development of sensor technology, fusion of multiple remote sensors has aroused wide attention in the earth observation area. In this article, we propose to integrate the complementary information of hyperspectral image (HSI) and infrared image (IFI) based on mathematical morphological methods. HSI contains rich spectral information and spatial information, but the operation methods using only hyperspectral data are still subject to many restrictions. IFI can capture infrared rays radiated in the object, but it has no advantage in dealing with complex terrain classification. HSI and IFI can acquire different information of objects, and the information between these two kinds of data has great complementarity. In order to make full use of the information provided by HSI and IFI, this article proposes an HSI and IFI collaborative classification framework based on a Threshold-based Local Contain Profile (TLCP), where TLCP is our new design for suppressing interferes within spatial extractions. Specifically, the spatial information of HSI and IFI is extracted by TLCP, and then, these features are integrated and fed into the support vector machine for object classification. Experimentally, we compare the proposed method with the existing LCP and EP and evaluate the collaborative framework using GF5 satellite data collected over Hebei Province in China. Final results demonstrated the effectiveness of the proposed method.