Hyper-Spectral Characteristics in Support of Object Classification and Verification - Zhang, Liren; Lu, Hao; Zhang, Hang; Zhao, Yuefeng; Xu, Huaqiang; Wang, Jingjing (IEEE access, 2019)
It is known that a typical hyper-spectral image consists of a sequence of wavelength bands and each wavelength band illustrates a two-dimensional image. This paper presents a HSI classification method including two approaches. The first approach is a probabilistic algorithm for object classification based on the image illustrated by single hyper-spectral wavelength bands, respectively, which generate a sequence of registered object images. Since hyper-spectral images (HSIs) obtained by air-borne or satellite-borne remote sensors has limited size, which may affect the classification accuracy, especially for small objects due to lack of pixels. The second approach investigates the hyper-spectral characteristics for a specific object in terms of uniformed hyper-spectrum energy function in wavelength domain. The hyper-spectral characteristics are applied to object verification, especially for objects which are difficult to distinguish from each other using classic classification methods, so that the accuracy is improved. The classification accuracy for the proposed object-oriented characterization method is evaluated in terms of producer accuracy (PA) and Kappa coefficient based on hyper-spectral images obtained from air-borne or satellite-borne remote sensors, respectively. The numerical results demonstrate that the proposed method can effectively identify and verify objects in hyper-spectral image, especially for those objects that are difficult to be distinguished from each other by classic methods. Furthermore, the proposed object classification method based on hyper-spectral characteristics is compared to conventional methods, including spectral information divergence (SID) method and multiple spectral angle mapper-Markov random fields (MSAM-MRF) method. It can be seen that the accuracy of object classification achieved by the proposed method can be up to 20% higher than that of classic methods.