Hyper spectral image classification using multi labelled, multi-scale and multi-angle CNN with MS-MA BT algorithm

Abstract

For classifying the hyperspectral image (HSI), convolution neural networks are used widely as it gives high performance and better results. For stronger prediction this paper presents new structure that benefit from both MS-MA BT (multi-scale multi-angle breaking ties) and CNN algorithm. We build a new MS-MA BT and CNN architecture. It obtains multiple characteristics from the raw image as an input. This algorithm generates relevant feature maps which are fed into concatenating layer to form combined feature map. The obtained mixed feature map is then placed into the subsequent stages to estimate the final results for each hyperspectral pixel. Not only does the suggested technique benefit from improved extraction of characteristics from CNNs and MS-MA BT, but it also allows complete combined use of visual and temporal data. The performance of the suggested technique is evaluated using SAR data sets, and the results indicate that the MS-MA BT-based multi-functional training algorithm considerably increases identification precision. Recently, convolution neural networks have proved outstanding efficiency on multiple visual activities, including the ranking of common two-dimensional pictures. In this paper, the MS-MA BT multi-scale multi-angle CNN algorithm is used to identify hyperspectral images explicitly in the visual domain. Experimental outcomes based on several SAR image data sets show that the suggested technique can attain greater classification efficiency than some traditional techniques, such as support vector machines and conventional deep learning techniques.