Spectral ? spatial urban target detection for hyperspectral remote sensing data using artificial neural network

Hyperspectral remote sensing is opening new gateways for a multitude of applications with an added advantage of high spectral and spatial resolution. Target detection of urban objects has gained prominence during the past decade for maintaining a pace with increasing urbanization. This paper aims to identify roads and roofs as urban targets using a hybrid approach of the spectral and spatial aspect of hyperspectral data. The work highlights a brief taxonomy of morphological operators namely, Dilation, Erosion, Opening and Closing with fused spectral signatures of urban targets considered. Artificial neural network (ANN) has been used as a machine learning measure due to its high prediction capability and its effectiveness over conventional target detection approaches. (c) 2021 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).