Class Specific Coders for Hyper-Spectral Image Classification

In this paper, we introduce the paradigm of class specific coders (CSC) for classification of hyper-spectral images (HSI). Apparently, CSC are defined as a set of distinct encoder-decoder (henceforth called a coder) networks where a given coder is trained on the samples of a particular class. In contrast to auto-encoders (AE) which learn an identity mapping of data in an unsupervised fashion, the CSC model, on the other hand, learns re-constructive mappings for all possible pairs of training samples for each class in separate coders. Further, for reducing redundancy, it is ensured that the latent space dimensions of the coders are orthogonal to each other. Once the CSC are trained, we introduce a feature encoding by concatenating the latent representations of all the class specific coders. Experimental results obtained on the benchmark Botswana and Indian Pines datasets ensure that the CSC model outperforms a number of AE variants significantly.