Deep Learning Hyperspectral Image Classification using Multiple Class-Based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations
John E. Ball,Pan Wei +1 more
- 11 Jul 2018
- pp 6903-6906
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TL;DR: A novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels and a deep neural network and morphological hole-filling to provide robust image classification.
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Abstract: Herein, we present a system for hyperspectral image segmentation that utilizes multiple class-based denoising autoen-coders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels. Finally, we utilize a deep neural network and morphological hole-filling to provide robust image classification. Results run on the Salinas dataset verify the high performance of the proposed algorithm,
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Deep Feature Learning for Hyperspectral Image Classification and Land Cover Estimation
Grigorios Tsagkatakis,Panagiotis Tsakalides +1 more
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TL;DR: This work proposes the introduction of the multi-label classification framework where classifiers are trained to predict multiple labels per pixel, and investigates the Stacked Sparse Autoencoders framework, an example of a deep learning network, for descriptive feature extraction.
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R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method
Bin Pan,Zhenwei Shi,Xia Xu +2 more
TL;DR: A novel simplified deep-learning model, rolling guidance filter (RGF) and vertex component analysis network (R-VCANet), is proposed, which achieves higher accuracy when the number of training samples is not abundant, especially when the training samples available are not abundant.