Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
TL;DR: This paper proposes a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery and reveals that the proposed models with sparse constraints provide competitive results to state-of-the-art methods.
read more
Abstract: Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data modeling. More importantly, we propose a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery. Finally, in order to further improve the performance, a virtual sample enhanced method is proposed. The proposed approaches are carried out on three widely used hyperspectral data sets: Indian Pines, University of Pavia, and Kennedy Space Center. The obtained results reveal that the proposed models with sparse constraints provide competitive results to state-of-the-art methods. In addition, the proposed deep FE opens a new window for further research.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis
TL;DR: Superpixel segmentation is a process of segmenting the spatial image into several semantic subregions with similar characteristic features, such grouping by similarity can significantly ease the subsequent processing steps.
SSA-SiamNet: Spectral–Spatial-Wise Attention-Based Siamese Network for Hyperspectral Image Change Detection
TL;DR: Wang et al. as discussed by the authors proposed an end-to-end Siamese CNN with a spectral–spatial-wise attention (SSA-SiamNet) mechanism.
62
LiteDepthwiseNet: A Lightweight Network for Hyperspectral Image Classification
TL;DR: LiteDepthwiseNet as discussed by the authors decomposes standard convolution into depthwise convolution and pointwise convolutions, and removes the ReLU layer and batch normalization layer to improve the overfitting phenomenon of the model on small-sized data sets.
61
Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets
TL;DR: An innovative approach termed the satellite-ground integrated destriping network (SGIDN) is proposed, for the first time, to mitigate the data dependency, so that a large set of striped-clean pairs is generated from the ground-based HSIs.
61
A deep heterogeneous feature fusion approach for automatic land-use classification
A Fotso Kamga Guy,A Fotso Kamga Guy,Tallha Akram,Bitjoka Laurent,Syed Rameez Naqvi,Mengue Mbom Alex,Nazeer Muhammad +6 more
TL;DR: A novel hybrid system for satellite image classification that combines the distinct information of deep features, and generate a discriminative representation by preserving the essential information of original feature space is proposed.
61
References
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
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.
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- 20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- 07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).