Multiscale Deep - CNN Based Target Prediction in Hyperspectral Image with Transfer Learning

Abstract

Abstract: There are different types of landscape throughout the world which is not readily or directly approachable for human being but their analysis to uncover factual information has become necessary for forming important decision when developing any fresh project. The geographical and landscape scenes can be adequately represented through hyperspectral images captured using remote sensors. The data in the images can potentially be both vast and intricate to analyze and it is essential to consistently perform adequate pre-processing. In this work, we have put up the use of deep learning and transfer learning for object prediction in hyperspectral data. There are mainly two algorithms that have been implemented in this research. The first method is based on Multi-Scale Deep CNN (Convolutional Neural Network) which takes hyperspectral data with varying sizes as the input to detect pixels whose intensity spreads uniformly over many wavelengths or may vary rapidly. Secondly, hyperspectral image sources are not readily available and can be expensive and there are also possibilities for high analysis complexity in the research, so a Transfer Learning based algorithm is applied to the DCNN model. Superior performance in accuracy was noted in the evaluation with respect to the F1 score and recall values for different objects fluctuate between 0.8 to 1.0. Further, we conducted a comparative study, pitting the proposed method against other state-of-the-art target prediction methodologies