Deep Cube-Pair Network for Hyperspectral Imagery Classification

Wei, Wei; Zhang, Jinyang; Zhang, Lei; Tian, Chunna; Zhang, Yanning

doi:10.3390/rs10050783

Cited by 31 publications

(19 citation statements)

References 53 publications

(76 reference statements)

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“…The existing research for HSI classification often concentrates the information in the spectral and spatial domains (e.g., 3-D CNNs proposed in [ 29 , 32 ]), and considers the spectral and spatial variabilities; however, the spatial hierarchies between features have seldom been explored. Spatial hierarchies may consist of features, sizes, places, contexts, pyramids, or even ultrametrics based on the spatially perceptive relationships.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Deng

Chen

et al. 2018

Sensors

126

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Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Deng

Chen

et al. 2018

Sensors

126

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“…Therefore, how to optimize the sample selection is another issue worth exploring in the future. Finally, more satellite remote sensing datasets and deep learning classifiers like cube-pair-based convolutional neural networks (CNN) [71] and CNN-Based Spatial Feature Fusion Algorithm (CSFF) [72] will be adopted in our framework to further promote the classification behaviors of our HCF.…”

Section: Discussionmentioning

confidence: 99%

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

et al. 2019

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Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

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“…Spatial-spectral features can be extracted using a deep convolutional neural network, and these features represent low-to-high level semantic information. Different deep learning architectures have also been introduced for HSIC [17][18][19][20][21][22][23]. Li et al [17] proposed a pixel-pair method to significantly increase the number of training samples.…”

Section: Introductionmentioning

confidence: 99%

“…This was necessary to overcome the imbalance between the high dimensionality of spectral features and limited training samples (also known as the Hughes phenomenon). A similar cube-pair 3-D convolution neural network (CNN) classification model has also been proposed [18]. Du et al proposed an unsupervised network to extract high-level feature representations without any label information [19].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

et al. 2019

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Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification.

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Deep Cube-Pair Network for Hyperspectral Imagery Classification

Cited by 31 publications

References 53 publications

Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

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