scholarly journals Classification of hyperspectral images by fusion of spectral and spatial features in convolutional neural networks

2021 ◽  
Vol 9 (2) ◽  
pp. 1-27
Author(s):  
Obeid Sharifi ◽  
◽  
Behnam Asghari Beirami ◽  
Mehdi Mokhtarzade ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hyperspectral Images ◽  
Spatial Features

CLASSIFICATION OF HYPERSPECTRAL IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

2021 ◽  
Vol 57 (2) ◽  
pp. 13-21
Author(s):  
V.I. Kozik ◽  
E.S. Nezhevenko
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hyperspectral Images

scholarly journals Pixel-Wise Classification of High-Resolution Ground-Based Urban Hyperspectral Images with Convolutional Neural Networks

2020 ◽  
Vol 12 (16) ◽  
pp. 2540
Author(s):  
Farid Qamar ◽  
Gregory Dobler
Keyword(s):  
Neural Networks ◽  
New York ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Urban Environments ◽  
Hyperspectral Images ◽  
Natural Materials ◽  
The Individual ◽  
Spectral Channels

Using ground-based, remote hyperspectral images from 0.4–1.0 micron in ∼850 spectral channels—acquired with the Urban Observatory facility in New York City—we evaluate the use of one-dimensional Convolutional Neural Networks (CNNs) for pixel-level classification and segmentation of built and natural materials in urban environments. We find that a multi-class model trained on hand-labeled pixels containing Sky, Clouds, Vegetation, Water, Building facades, Windows, Roads, Cars, and Metal structures yields an accuracy of 90–97% for three different scenes. We assess the transferability of this model by training on one scene and testing to another with significantly different illumination conditions and/or different content. This results in a significant (∼45%) decrease in the model precision and recall as does training on all scenes at once and testing on the individual scenes. These results suggest that while CNNs are powerful tools for pixel-level classification of very high-resolution spectral data of urban environments, retraining between scenes may be necessary. Furthermore, we test the dependence of the model on several instrument- and data-specific parameters including reduced spectral resolution (down to 15 spectral channels) and number of available training instances. The results are strongly class-dependent; however, we find that the classification of natural materials is particularly robust, especially the Vegetation class with a precision and recall >94% for all scenes and model transfers and >90% with only a single training instance.

Smart feature extraction and classification of hyperspectral images based on convolutional neural networks

2020 ◽  
Vol 14 (10) ◽  
pp. 1999-2005 ◽  
Author(s):  
Maissa Hamouda ◽  
Karim Saheb Ettabaa ◽  
Med Salim Bouhlel
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Convolutional Neural Networks ◽  
Hyperspectral Images

Pixel-Based Classification of Hyperspectral Images Using Convolutional Neural Networks

2019 ◽  
Vol 87 (1-2) ◽  
pp. 33-45
Author(s):  
Syed Aamer Hussain ◽  
Ali Tahir ◽  
Junaid Aziz Khan ◽  
Ahmad Salman
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hyperspectral Images

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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