Semisupervised Neural Networks for Efficient Hyperspectral Image Classification

Ratle, Frédéric; Camps‐Valls, Gustau; Weston, Jason

doi:10.1109/tgrs.2009.2037898

Cited by 307 publications

(142 citation statements)

References 29 publications

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“…In the earlier research, different pixel-wise approaches have been developed [8][9][10][11]. However, without considering the spatial information, the obtained classification results by these approaches usually contain much noise.…”

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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Section: Introductionmentioning

confidence: 99%

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Zhang

et al. 2017

Remote Sensing

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“…Many popular methods have been developed for the hyperspectral image classification in the past several decades . One of the main approaches in this context is the use of only spectral information within each pixel within a popular classifier, such as multinomial logistic regression (MLR) [6,7,8], neural networks [9,10], support vector machines (SVMs) [11,12], graph method [13,14], AdaBoost [15], Gaussian process approach [16] and random forest [17].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid DBN and CRF Model for Spectral-Spatial Classification of Hyperspectral Images

Zhong

Gong²

2017

Stat., optim. inf. comput.

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Hyperspectral image classification plays an important role in remote sensing image analysis. Recent techniques have attempted to investigate the capabilities of deep learning approaches to tackle the hyperspectral image classification. This work shows how to further improve the hyperspectral image classification through using both a deep representation and contextual information. To implement this objective, this work proposes a new Conditional Random Field (CRF) model (named DBN-CRF) with the potentials defined over the deep features produced by a Deep Belief Network (DBN). The newly formulated DBN-CRF model takes advantage of the strength of DBNs in learning a good representation and the ability of CRFs to model contextual (spatial) information in both the observations and labels. Within a piecewise training framework, an efficient training method is proposed to train the whole DBN-CRF model end-to-end. This means that the parameters in DBN and CRF can be jointly trained and thus the proposed method can fully use the strength of both DBN and CRF. Moreover, in the proposed training method, the end-to-end training can be implemented with a standard back-propagation algorithm, avoiding the repeated inference usually involved in CRF training and thus is computationally efficient. Experiments on real-world hyperspectral data show that our method outperforms the most recent approaches in hyperspectral image classification.

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“…Composite kernels have been studied for enhancing hyperspectral image classification in [12]. Kernel sparse representation based models [13], semisupervised graph-based approaches [14], semisupervised neural networks [15], and manifold learning-based studies [16] have been proposed for efficient hyperspectral image classification in recent years.…”

Section: Introductionmentioning

confidence: 99%

Integration of spectral and spatial information via local covariance matrices for segmentation and classification of hyperspectral images

Ergül¹,

Bilgin²

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this work, a novel approach is presented for the feature extraction step in hyperspectral image processing to form more discriminative features between different pixel regions. The proposed method combines both spatial and spectral information, which is very important for segmentation and classification of hyperspectral images. For comparison, five different feature sets are formed using eigen decomposition of local covariance matrices of subcubes located around a pixel of interest in the scene. Subcubes of neighbor pixels are obtained by a windowed structure to expose pattern similarities. As a novel approach, local covariance matrices are computed in eigenspace and proposed feature sets are created after this stage. Before the formation of feature sets in eigenspace, the original input space is transferred to eigenspace by linear and nonlinear manner by principal component analysis (PCA) and its kernelized version (KPCA) and they are used in the experiments comparatively. In the simulations, one hyperspectral scene with ground-truth and one without ground-truth are used for the segmentation and classification tasks. Results of experiments are evaluated with four different unsupervised learning algorithms for data without ground-truth and three different supervised learning algorithms for data with ground-truth comparatively.

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Semisupervised Neural Networks for Efficient Hyperspectral Image Classification

Cited by 307 publications

References 29 publications

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

A Hybrid DBN and CRF Model for Spectral-Spatial Classification of Hyperspectral Images

Integration of spectral and spatial information via local covariance matrices for segmentation and classification of hyperspectral images

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