Abstract:
Processing of hyperspectral remote sensing datasets poses challenges in terms of computational expense pertaining to data redundancy. As such, band selection becomes indispensable to address redundancy while preserving the optimal spectral information. This paper proposes a novel architecture using Genetic Algorithm (GA) optimizing technique with Random Forest (RF) classifier for efficient band selection with the Hyperspectral Precursor of the Application Mission (PRISMA) dataset. The optimal bands are BLUE (λ = 492.69 nm), NIR (λ = 959.52 nm), and SWIR 1 (λ = 1626.78 nm). This paper also involves an application of the selected bands to accurately identify and quantify built-up pixels by means of a new spectral index named Hyperspectral Imagery-based Built-up Index (HIBI). The proposed index was used to map built-up pixels in six cities around the world namely Jaipur, Varanasi, Delhi, Tokyo, Moscow and Jakarta to establish its robustness. This analysis shows that the proposed index has an accuracy of 94.02%, higher than all the other indices considered for this study. Moreover, the spectral separability analysis also establishes the efficiency of the proposed index to differentiate built-up pixels from spectrally similar land use or land cover classes.
