Journal ArticleDOI
Smart feature extraction and classification of hyperspectral images based on convolutional neural networks
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TLDR
A smart feature extraction (SFE) and classification by convolutional neural network (2D-CNN) method made up of two parts that consists in reducing spectral information by a probabilistic method based on the Softmax function.Abstract:
Hyperspectral satellite imagery (HSI) is an advanced technology for object detection because it provides a large amount of information. Thus, the classification of HSIs is very complicated, so the methods of reducing spectral or spatial information generally degrade the quality of classification. In order to solve this problem and guarantee faster and more efficient processing, we propose a smart feature extraction (SFE) and classification by convolutional neural network (2D-CNN) method made up of two parts. The first consists in reducing spectral information by a probabilistic method based on the Softmax function. The second is classification by processing batches of data in the proposed CNN network. The method was tested on two public hyperspectral images (Indian Pines and SalinasA) to prove its effectiveness in increasing classification accuracy and reducing computing time.read more
Citations
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Proceedings ArticleDOI
Classification of Hyperspectral Images Using 3D CNN Based ResNet50
Hüseyin Firat,Davut Hanbay +1 more
TL;DR: In this paper, the 3D convolutional neural network (CNN) based ResNet50 method is proposed to solve the problems encountered in hyperspectral studies and to extract sufficient spatial spectral properties from the network.
Book ChapterDOI
Dual Convolutional Neural Networks for Hyperspectral Satellite Images Classification (DCNN-HSI)
Maissa Hamouda,Med Salim Bouhlel +1 more
TL;DR: A new approach to the reduction and classification of HSI is proposed consisting of a dual Convolutional Neural Networks (DCNN), which aims to improve precision and computing time.
References
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