Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features

Liang, Heming; Li, Qi

doi:10.3390/rs8020099

Cited by 211 publications

(110 citation statements)

References 34 publications

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“…In order to deal with this problem, DR methods are usually applied to reduce the spectral dimensionality prior to 2D-CNN being employed for feature extraction and classification [33][34][35]. For instance, in [33], the first three principal components (PCs) are extracted from HSI by PCA, and then a 2D-CNN is used to extract deep features from condensed HSI with a window size of 42 × 42 in order to predict the label of each pixel.…”

Section: D Convolution Operationmentioning

confidence: 99%

“…This was carried out prior to the 2D-CNN being used to extract deep features from the compressed HIS (with a window size of 5 × 5), and subsequently to complete the classification task. Furthermore, the approach presented in [35] requires three computational steps: The high-level features are first extracted by a 2D-CNN, where the entire HSI is whitened with the PCA algorithm, retaining the several top bands; the sparse representation technique is then applied to further reduce the high level spatial features generated by the first step. Only after these two steps are classification results obtained based on learned sparse dictionary.…”

Section: D Convolution Operationmentioning

confidence: 99%

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Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

2017

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Abstract:Recent research has shown that using spectral-spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral-spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral-spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods-namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods-on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.

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Section: D Convolution Operationmentioning

confidence: 99%

Section: D Convolution Operationmentioning

confidence: 99%

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

2017

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“…Hyperspectral image (HSI) [1][2][3][4] contains hundreds of narrow bands, and has been extensively used in different application domains, such as forest monitoring and mapping [5,6], land-use classification [7,8], anomaly detection [9], endmember extraction [10] and environment monitoring [11]. Among those kinds of applications, supervised classification is a fundamental task and has been widely studied over the past decades [12,13].…”

Section: Introductionmentioning

confidence: 99%

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Hu¹,

He²,

Li³

et al. 2018

Remote Sensing

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Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the "internal uncertainty" of each independent view, and adopt the inconsistencies between views to estimate the "external uncertainty". Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.

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“…In these different signal processing tasks, state-of-the-art performance is usually obtained. Recently, sparse representation classification (SRC) has also attracted much attention for the classification of the HSI [27,[32][33][34][35]. The SRC assumes that a test pixel can be approximately represented by a linear combination of all training samples.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the tensor-based classification methods have been used for the HSI [22][23][24]. Moreover, with the rise of deep learning, spectral-spatial information-based deep learning algorithms [25][26][27] are also applied in the HSI classification, which can extract potential and invariant features of the HSI.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Zhang

et al. 2017

Remote Sensing

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Abstract:Recently, superpixel segmentation has been proven to be a powerful tool for hyperspectral image (HSI) classification. Nonetheless, the selection of the optimal superpixel size is a nontrivial task. In addition, compared with single-scale superpixel segmentation, the same image segmented on a different scale can obtain different structure information. To overcome such a drawback also utilizing the structural information, a multiscale superpixel-based sparse representation (MSSR) algorithm for the HSI classification is proposed. Specifically, a modified segmentation strategy of multiscale superpixels is firstly applied on the HSI. Once the superpixels on different scales are obtained, the joint sparse representation classification is used to classify the multiscale superpixels. Furthermore, majority voting is utilized to fuse the labels of different scale superpixels and to obtain the final classification result. Two merits are realized by the MSSR. First, multiscale information fusion can more effectively explore the spatial information of HSI. Second, in the multiscale superpixel segmentation, except for the first scale, the superpixel number on a different scale for different HSI datasets can be adaptively changed based on the spatial complexity of the corresponding HSI. Experiments on four real HSI datasets demonstrate the qualitative and quantitative superiority of the proposed MSSR algorithm over several well-known classifiers.

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Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features

Cited by 211 publications

References 34 publications

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

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