Journal ArticleDOI
Deep Learning-Based Classification of Hyperspectral Data
Reads0
Chats0
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
More filters
Journal Article
Deep Learning Network Based Spectrum Sensing Methods for OFDM Systems
TL;DR: A stacked autoencoder based spectrum sensing method using time-frequency domain signals (SAE-TF) is proposed, in which SAE-SS achieves higher sensing accuracy than SAW-SS at the cost of higher computational complexity.
Book ChapterDOI
Using CNN to Classify Hyperspectral Data Based on Spatial-spectral Information
Lianlei Lin,Xinyi Song +1 more
TL;DR: In this article, the spectral and spatial information is combined and used for hyperspectral image classification, which has been successfully applied in image recognition and language detection, and the experiments on KSC and Pavia U data sets demonstrate the feasibility and efficacy of convolutional neural network (CNN) in hyperspectra image classification.
Journal ArticleDOI
Distributed sequence memory of multidimensional inputs in recurrent networks
TL;DR: In this article, the authors provide general results characterizing the STM capacity for linear echo state networks with multidimensional input streams when the inputs have common low-dimensional structure: sparsity in a basis or significant statistical dependence between inputs.
Journal ArticleDOI
An attention-driven convolutional neural network-based multi-level spectral–spatial feature learning for hyperspectral image classification
Chunyu Pu,Hong Huang,Liping Yang +2 more
TL;DR: Wang et al. as discussed by the authors proposed an attention mechanism-based method termed multi-level feature network with spectral-spatial attention model (MFNSAM), which consists of a multilevel feature CNN (MFCNN) and a spectral-space attention module (SSAM).
Journal ArticleDOI
Robust Self-Ensembling Network for Hyperspectral Image Classification
TL;DR: Wang et al. as mentioned in this paper proposed a robust self-ensembling network (RSEN), which consists of two subnetworks including a base network and an ensemble network, with the constraint of both the supervised loss from the labeled data and the unsupervised loss from unlabeled data.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
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.
Journal ArticleDOI
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
Journal ArticleDOI
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
Related Papers (5)
Deep Convolutional Neural Networks for Hyperspectral Image Classification
Classification of hyperspectral remote sensing images with support vector machines
Farid Melgani,Lorenzo Bruzzone +1 more