Journal ArticleDOI
Deep Learning-Based Classification of Hyperspectral Data
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TLDR
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.Abstract:
Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral-spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods' huge potential for accurate hyperspectral data classification.read more
Citations
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Journal ArticleDOI
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
TL;DR: This work focuses on theories, tools, and challenges for the RS community, and focuses on unsolved challenges and opportunities as they relate to inadequate data sets, big data, and human-understandable solutions for modeling physical phenomena.
Journal ArticleDOI
Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN
TL;DR: An unsupervised feature extraction framework, named as patch-to-patch convolutional neural network (PToP CNN), is proposed for collaborative classification of hyperspectral and LiDAR data and provides superior performance when compared with some state-of-the-art classifiers, such as two-branch CNN and context CNN.
Journal ArticleDOI
Active Deep Learning for Classification of Hyperspectral Images
Peng Liu,Hui Zhang,Kie B. Eom +2 more
TL;DR: The proposed active learning algorithm based on a weighted incremental dictionary learning that trains a deep network efficiently by actively selecting training samples at each iteration is shown to be efficient and effective in classifying hyperspectral images.
Journal ArticleDOI
Spectral–Spatial Attention Network for Hyperspectral Image Classification
TL;DR: A spectral–spatial attention network (SSAN) is proposed to capture discriminative spectral-spatial features from attention areas of HSI cubes to outperforms several state-of-the-art methods.
Journal ArticleDOI
Using deep learning to predict soil properties from regional spectral data
TL;DR: In this article, the authors developed and evaluated convolutional neural networks (CNNs), a type of deep learning algorithm, as a new way to predict soil properties from raw soil spectra.
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