Proceedings Article10.1109/ICVRV.2016.16
Efficient Deep Auto-Encoder Learning for the Classification of Hyperspectral Images
Atif Mughees,Linmi Tao +1 more
- 01 Sep 2016
- pp 44-51
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TL;DR: Experimental results with widely-used hyperspectral data confirms that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies.
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Abstract: Hyperspectral Image (HSI) classification is one of the most pervasive issue in hyperspectral remote sensing field. Deep learning is an efficient learning algorithm that has been recently applied to HSI classification. This paper proposes a new spectralspatial HSI classification method based on the deep features extraction using stacked-auto-encoders (SAE) and unsupervised HIS segmentation. Specifically, first the SAE model is exploited as a classical spectral information-based classifier to extract the deep features. Second, spatial dominated information is extracted by using effective boundary adjustment based segmentation technique. Finally, maximum voting criteria is used to merge the extracted spectral and spatial features, which results into the accurate spectral-spatial HSI classification. Experimental results with widely-used hyperspectral data confirms that the new spectral and spatial classification approach is able to improve results significantly in terms of classification accuracies.
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Citations
Deep learning classifiers for hyperspectral imaging: A review
TL;DR: A comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature is provided, providing an exhaustive comparison of the discussed techniques.
903
A survey: Deep learning for hyperspectral image classification with few labeled samples
TL;DR: Although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model.
Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands.
Tien-Heng Hsieh,Jean-Fu Kiang +1 more
TL;DR: The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data.
95
Deep neural network-based domain adaptation for classification of remote sensing images
Li Ma,Jiazhen Song +1 more
TL;DR: The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data, demonstrating the effectiveness of the proposed approach.
70
3D residual spatial–spectral convolution network for hyperspectral remote sensing image classification
TL;DR: A hybrid 3D residual spatial–spectral convolution network (3D-RSSCN) is proposed to extract deep spatiospectral features using 3D CNN and ResNet18 architecture, and principal component analysis (PCA) is used as the preprocessing step for optimum spectral band extraction.
42
References
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Classification of hyperspectral remote sensing images with support vector machines
Farid Melgani,Lorenzo Bruzzone +1 more
TL;DR: This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces and concludes that SVMs are a valid and effective alternative to conventional pattern recognition approaches.
Deep Learning-Based Classification of Hyperspectral Data
TL;DR: The concept of deep learning is introduced into hyperspectral data classification for the first time, and a new way of classifying with spatial-dominated information is proposed, which is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression.
2.6K
•Book
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Chein-I Chang
- 01 Jan 2003
TL;DR: In this article, a quantitative analysis of mixed-to-pure pixel conversion is presented, along with an anomaly detection method for unsupervised mixed pixel classification and a projection pursuit method for projection pursuit.
1.4K
Advances in Spectral-Spatial Classification of Hyperspectral Images
Mathieu Fauvel,Yuliya Tarabalka,Jon Atli Benediktsson,Jocelyn Chanussot,James C. Tilton +4 more
- 01 Mar 2013
TL;DR: Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper and several techniques are investigated for combining both spatial and spectral information.