Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images

Morsier, Frank de; Borgeaud, M.; Gass, Volker; Thiran, Jean‐Philippe; Tuia, Devis

doi:10.1109/tgrs.2016.2517242

Cited by 66 publications

(31 citation statements)

References 44 publications

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“…The optimal solutions are acquired by solving generalized eigenvalue problems in the same manner [78]. These methods can be further expanded to shape a manifold learning method by using the kernel trick, similarly to the approaches in [79]. Table II and Fig.…”

Section: Semisupervised Drmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

625

266

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Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the analysis of large areas of the Earth surface feasible. However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions, make such data inherently nonlinear and complex, which poses extreme challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing. For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validation and evaluation of different methodologies, followed by a discussion of future challenges and research directions.

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Section: Semisupervised Drmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

625

266

View full text Add to dashboard Cite

show abstract

“…structure (by the low-rankness) and the locally linear structure (by the sparseness) of data, hence it is both generative and discriminative. Morsier et al [27] presented a kernel low-rank and sparse graph, which was based on sample proximities in reproducing kernel Hilbert spaces and expressed sample relationships under sparse and low-rank constraints. However, data class structure is not considered in the above methods.…”

Section: Hsi Classification Based On Sblsmentioning

confidence: 99%

“…Recently, deep learning (DL) is found to be able to automatically learn representative features from data via stacking multi-layer nonlinear units [10,11], making successful application on HSI Morsier et al [27] presented a kernel low-rank and sparse graph, which was based on sample proximities in reproducing kernel Hilbert spaces and expressed sample relationships under sparse and low-rank constraints. However, data class structure is not considered in the above methods.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System

Wang

Cheng

et al. 2018

Remote Sensing

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Recently, deep learning-based methods have drawn increasing attention in hyperspectral imagery (HSI) classification, due to their strong nonlinear mapping capability. However, these methods suffer from a time-consuming training process because of many network parameters. In this paper, the concept of broad learning is introduced into HSI classification. Firstly, to make full use of abundant spectral and spatial information of hyperspectral imagery, hierarchical guidance filtering is performing on the original HSI to get its spectral-spatial representation. Then, the class-probability structure is incorporated into the broad learning model to obtain a semi-supervised broad learning version, so that limited labeled samples and many unlabeled samples can be utilized simultaneously. Finally, the connecting weights of broad structure can be easily computed through the ridge regression approximation. Experimental results on three popular hyperspectral imagery datasets demonstrate that the proposed method can achieve better performance than deep learning-based methods and conventional classifiers.

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“…One of the state-of-the-art algorithms is the transductive support vector machine (TSVM) [19][20][21]. (3) Graph-based methods [22][23][24][25][26] that utilize labeled and unlabeled samples to construct graphs and minimize the energy function, and thus, assigning labels to unlabeled samples. (4) Wrapper-based methods, which apply a supervised learning method iteratively and a certain amount of unlabeled samples are labeled in each iteration.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

He¹,

Liu²,

Wang³

et al. 2017

Remote Sensing

140

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Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method, especially with a limited number of labeled samples.

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Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images

Cited by 66 publications

References 44 publications

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Hyperspectral Imagery Classification Based on Semi-Supervised Broad Learning System

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

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