Hyperspectral datasets provide explicit ground covers with hundreds of bands. Filtering contiguous hyperspectral datasets potentially discriminates surface features. Therefore, in this study, a number of spectral bands are minimized without losing original information through a process known as dimensionality reduction (DR). Redundant bands portray the fact that neighboring bands are highly correlated, sharing similar information. The benefits of utilizing dimensionality reduction include the ability to slacken the complexity of data during processing and transform original data to remove the correlation among bands. In this paper, two DR methods, principal component analysis (PCA) and minimum noise fraction (MNF), are applied to the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset of Kalaburagi for discussion.

Land use/land cover (LULC) is a significant factor which plays a vital role in defining an urban ecosystem. Interpretations of LULC are eased in recent times by utilizing hyperspectral and multispectral datasets obtained from various platforms. An attempt is made to comparatively assess the potentiality of AVIRIS NG with Sentinel 2 data through applied classification techniques for Kalaburagi urban sphere. Spectral responses of both datasets were analyzed to derive reflectance spectra. A standard supervised classification algorithm associated with dimensionality reduction techniques is applied. For performance evaluation, results are validated to check which dataset outperforms well and provides better accuracy.

Flash floods in the Himalayan Rivers result in hundreds of deaths causing a sudden hazard in a minimum period of time. These hydrological events of mostly happen due to cloudburst incidents in the Indian Himalayas, with an unexpected heavy overwhelming of precipitation in a short interval over a small region. These extreme hydrological events are assessed through the analytical hierarchy process for the upper stream catchments of Tehri Dam and Srinagar. The morphometry characteristics of these catchments are collaboratively integrated with the SAC (Space Application Centre) hydro simulated discharge and rainfall data to identify the flash-flood-vulnerable hazard region over the surrounding catchment regions.