Spectral & Spatial Feature detection on Hyperspectral Images using Deep Neural Networks

Abstract

Hyperspectral is one of such techniques, which is used mainly when the images from the satellite are captured and are used to identify different objects. Hyperspectral images are made up of a large number of bands naturally. Thus extracting information from the satellite comes with many problems and challenges. But with the use of powerful deep learning (DL) methods, earth's surface can be precisely explored and analyzed. The combination of spatial and spectral information helps to track and find out remotely sensed scrutinized data everywhere. In this paper, different Deep Neural Network (DNN) models like Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and 3D Convolution Neural Network are compared for the purpose of Hyperspectral image classification.