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The Accuracy Improvement of the Hyperspectral Satellite Image Classification by Using the Development of a Convolutional Neural Network and Deep Learning
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Somayeh Mahmoudi   , Najmeh Neysani Samany * , Ara Toomanian |
| University of Tehran |
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Abstract: (2006 Views) |
| Nowadays, with the advancement of technology, numerous sensors provide high spatial and spectral resolution images. So far, several methods have been proposed for hyperspectral image classification, each of which seeks to overcome a number of computational and processing challenges of hyperspectral data. The efficiency of multi-layer perceptron neural networks is greatly reduced due to the increase in the number of parameters along with the increase of the layers, which is essential in complex topics such as hyperspectral image classification. In recent years, the concept of deep learning, especially convolutional neural networks (CNN), has attracted the attention of pattern recognition researchers due to the automatic generation of features and the reduction of parameters compared to the multi-layer perceptron neural networks by sharing the parameters in each layer. The goal of the present study is to develop a convolutional neural networks (CNN in order to classify hyperspectral images. The innovation of this study is to provide a framework to use deep learning. The proposed framework includes four steps. The first step is to reduce dimension by using the sub-space method, the second step is to prepare the CNN inputs, the third step is to augment the teaching data, and the fourth step is to design the CNN architecture. Implementation of the proposed framework on the benchmark data of the University of Pavia, despite the use of a limited number of educational data, led to the classification accuracy of 98.3%. |
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| Keywords: Hyperspectral Images, Classification, Deep Learning, Convolutional Neural Network. |
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Full-Text [PDF 2277 kb]
(744 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2021/01/14 | Accepted: 2022/03/7 | Published: 2022/03/8
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