Title: Multi spectral image classification based on deep feature extraction using deep learning technique

Authors: Y. Muralimohanbabu; K. Radhika

Addresses: ECE Department, SITAMS, Chittoor, AP, 517127, India ' ECE Department, GIST, Nellore, AP, 533003, India

Abstract: Remote sensing image classification accuracy depends on the extraction of Deep Feature Extraction. Unsupervised deep feature extraction employs single-layer and deep convolutional networks. Application of supervised convolutional networks is highly challenging for multi- and hyper-spectral imagery when input data dimensionality is high and labelled set is limited. To accomplish the mentioned, greedy layer-wise unsupervised pre-training combined with an appropriate algorithm for unsupervised learning of sparse features is proposed. This algorithm concentrates on sparse representations and sparsity of the extracted features at a time. The proposed method is applied for land use/cover classification of different spatial/spectral remote imagery. Comparing the current algorithms for classification, the proposed method performs well. Extraction of powerful discriminative features is possible with single-layer convolutional networks to obtain detailed results in classification. Different spatial/spectral parameters are calculated to quantify the results.

Keywords: unsupervised learning; deep learning; multispectral images; segmentation; sparse features learning; classification.

DOI: 10.1504/IJBRA.2021.117169

International Journal of Bioinformatics Research and Applications, 2021 Vol.17 No.3, pp.250 - 261

Received: 12 Dec 2018
Accepted: 03 Jun 2019

Published online: 13 Aug 2021 *

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