Generation of Sub-Pixel-Level Maps for Mixed Pixels in Hyperspectral Image Data

Hyperspectral data can find wide applications in classification and mapping of pure and mixed pixels in images of different land-cover types. Hyperspectral data of high spectral resolution enhance discrimination of target objects; but the low spatial resolution poses a challenge due to creation of mixed pixels. The cost of acquiring images at high resolution from sensors is high and rarely available. With images of coarser spatial resolution, it is difficult to identify the endmembers and their locations within the mixed pixel. This study utilizes the fractional abundance values of target endmembers obtained from linear spectral unmixing in locating the sub-pixels of a mixed pixel. The study illustrates the preparation of classified maps of finer spatial resolution by locating the sub-pixels through different mapping algorithms. A comparative analysis of these mapping algorithms, viz. attraction model-based sub-pixel mapping, simulated annealing, neighbourhood connectivity, cosine similarity-based mapping and Markov random fieldbased mapping has been made and an output generated. The algorithms have been implemented on standard hyperspectral datasets of Indian Pines having 200 spectral channels, Pavia University of 103 spectral channels and Jasper Ridge of 198 spectral channels. It has been observed that simulated annealing-based mapping produces higher accuracy rate than the other algorithms, whereas in terms of execution time, attraction model takes lesser time. The accuracy has been validated with the ground reference map of available standard hyperspectral datasets on which each algorithm has been tested and analysed.