CNN based spectral super-resolution of remote sensing images

The spectral super-resolution techniques attempt to re-project spectrally coarse images to a set of finer wavelength bands. However, complexity of the mapping between coarser and finer scale spectra, large variability of spectral signatures, and the difficulty in simultaneously modeling spatial and spectral contexts make the problem highly ill-posed. Our main hypothesis is that the consideration of spatial as well as spectral aspects is essential for spectral enhancement of remote sensing images. In this regard, this paper proposes a framework consisting of sparse-coding-based pixel-spectra enhancement, collaborative unmixing, and spatial-spectral prior based transformation. Two sparse-coding-based architectures are proposed to project the coarser scale pixel-spectra to the target scale. These models facilitate simultaneous optimization of sparse codes and dictionaries with regard to the spectral super-resolution objective. A CNN based encoding-decoding architecture is explored to model the spatial-spectral prior for improving fidelity of the reconstructions. The endmember similarities and spectral image prior are considered while designing the proposed loss functions. The experiments, over standard as well as AVIRIS-NG and drone-derived datasets, confirm better accuracy of the proposed frameworks as compared to the prominent approaches. In addition, the proposed CNN models for spectral upscaling and spatial-spectral transformation are found to be less sensitive to the variation in network parameter values.