Book ChapterDOI
Dual Convolutional Neural Networks for Hyperspectral Satellite Images Classification (DCNN-HSI)
Maissa Hamouda,Med Salim Bouhlel +1 more
- pp 369-376
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TLDR
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.Abstract:
Hyperspectral Satellite Images (HSI) presents a very interesting technology for mapping, environmental protection, and security. HSI is very rich in spectral and spatial characteristics, which are non-linear and highly correlated which makes classification difficult. In this paper, we propose a new approach to the reduction and classification of HSI. This deep approach consisting of a dual Convolutional Neural Networks (DCNN), which aims to improve precision and computing time. This approach involves two main steps; the first is to extract the spectral data and reduce it by CNN until a single value representing the active pixel is displayed. The second consists in classifying the only remaining spatial band on CNN until the class of each pixel is obtained. The tests were applied to three different hyperspectral data sets and showed the effectiveness of the proposed method.read more
Citations
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Journal ArticleDOI
Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra-Convolutional Neural Networks)
Maissa Hamouda,Med Salim Bouhlel +1 more
Proceedings ArticleDOI
ISAR-Image recognition using optimized HONN by a Metaheuristic algorithm
TL;DR: In this article , the Functional Link Artificial Neural Network (FLANN), a higher order neural network, was optimized using a revolutionary metaheuristic inspired by the Firefly algorithm to identify radar targets.
Proceedings ArticleDOI
ISAR-Image recognition using optimized HONN by a Metaheuristic algorithm
TL;DR: The Functional Link Artificial Neural Network (FLANN), a higher order neural network, was optimized in this paper using a revolutionary metaheuristic inspired by the Firefly algorithm to identify radar targets.
Journal ArticleDOI
Deep Transfer Learning of Satellite Imagery for Land Use and Land Cover Classification
TL;DR: In this article , the authors performed land use landcover (LULC) classification using the training data of satellite imagery for the Moscow region and compared the accuracy attained from different models.
References
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Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
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Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Journal ArticleDOI
Hyperspectral Image Classification With Attention-Aided CNNs
TL;DR: An attention-aided CNN model based on the traditional CNN model that incorporates attention modules to aid networks that focus on more discriminative channels or positions for spectral and spatial classifications of hyperspectral images is proposed.
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