Dual Convolutional Neural Networks for Hyperspectral Satellite Images Classification (DCNN-HSI)

Hamouda, Maissa; Bouhlel, Med Salim

doi:10.1007/978-3-030-63820-7_42

Cited by 5 publications

(2 citation statements)

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“…Finally, the application of different spectral reduction methods like smart feature extraction (SFE-CNN) [25], where is a probabilist reduction of the different spectral values of each spatial location of the image, to obtain at the end a single spatial plane (2D image), or deep-dual extraction (DCNN) [26], where first a CNN is used (1D) for the extraction of the spectral information, then the reduction of characteristics, and a second CNN (2D) for the extraction of the spatial information, then the classification of characteristics.…”

Section: Classification In One or More Dimensionsmentioning

confidence: 99%

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Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

Hamouda

Bouhlel

2021

IET Image Processing

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Classification of Hyperspectral Satellite Images (HSI) is a very important technology for object detection and cartography. Several problems can be detected, which make classification difficult (large size of the images, fusion between the classes, small amount of samples, etc.). Recently, several Convolutional Neural Networks (CNN-HSI) have been proposed for the classification of hyperspectral images. In this article, an improvement to CNN-HSI is proposed, aiming to reduce the number of erroneous pixels during classification (due to the limited number of samples). Thus, an extra-convolution technique (ExCNN) is proposed, where we add layers of global convolutions on the classified images, outgoing from classical CNN. The addition of 1 to 10 layers, on three real hyperspectral images, is tested. The results obtained are compared with other similar methods of the state of art, and show the effectiveness of the proposed method. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Classification In One or More Dimensionsmentioning

confidence: 99%

“…In this section we present the applied work steps. We use the same architecture applied in SFE-CNN [25] and DCNN [26]. Thus, our network is composed of three layers of convolutions and ReLU, two layers of subdivision (MaxPooling), two layers of fully connected, and one layer of softmax.…”

Section: Proposed Approach (Excnn)mentioning

confidence: 99%