A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction

Ou, Depin; Tan, Kun; Du, Qian; Zhu, Jishuai; Wang, Xue; Chen, Yu

doi:10.3390/rs11060654

Cited by 19 publications

(7 citation statements)

References 41 publications

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“…The Indian Pines data set [ 30 , 31 ] and Salinas scene data set [ 2 , 30 ] were the scenes gathered by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Indian Pines consisted of

pixels and 220 spectral bands.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Liu

Xia

et al. 2020

Sensors

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Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve classification accuracy, an improved spatial–spectral weight manifold embedding (ISS-WME) algorithm, which is based on hyperspectral data with their own manifold structure and local neighbors, is proposed in this study. The manifold structure was constructed using the structural weight matrix and the distance weight matrix. The structural weight matrix was composed of within-class and between-class coefficient representation matrices. These matrices were obtained by using the collaborative representation method. Furthermore, the distance weight matrix integrated the spatial and spectral information of HSIs. The ISS-WME algorithm describes the whole structure of the data by the weight matrix constructed by combining the within-class and between-class matrices and the spatial–spectral information of HSIs, and the nearest neighbor samples of the data are retained without changing when embedding to the low-dimensional space. To verify the classification effect of the ISS-WME algorithm, three classical data sets, namely Indian Pines, Pavia University, and Salinas scene, were subjected to experiments for this paper. Six methods of dimensionality reduction (DR) were used for comparison experiments using different classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The experimental results show that the ISS-WME algorithm can represent the HSI structure better than other methods, and effectively improves the classification accuracy of HSIs.

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pixels and 220 spectral bands.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Liu

Xia

et al. 2020

Sensors

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“…A novel tri-training semi-supervised hyperspectral image classification method based on regularized local discriminant embedding feature extraction (RLDE) was proposed in [64]. In this work, the RLDE process is used for optimal number of feature extraction to overcome the limitation of singular values and over-fitting of local Fisher discriminant analysis and local discriminant embedding.…”

Section: Comparison and Discussionmentioning

confidence: 99%

Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Ahmad

Khan

et al. 2019

Remote Sensing

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Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.

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“…The more detailed information on feature coding methods and feature f u s i o n s t r a t e g i e s c a n b e f o u n d i n L i e t a l . (2016,2017) and Du et al (2019).…”

Section: Supervised Feature Learning-based Methodsmentioning

confidence: 97%

“…Recently, based on regularized local discriminant embedding (RLDE), Ou et al (2019) proposed a novel tri-training algorithm. To solve the problems of over-fitting and singular values, RLDE is used for the extraction of optimal features.…”

Section: Semi-supervised Learning For Hyperspectral Image Classificationmentioning

confidence: 99%

Advances of Four Machine Learning Methods for Spatial Data Handling: a Review

et al. 2020

Self Cite

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Most machine learning tasks can be categorized into classification or regression problems. Regression and classification models are normally used to extract useful geographic information from observed or measured spatial data, such as land cover classification, spatial interpolation, and quantitative parameter retrieval. This paper reviews the progress of four advanced machine learning methods for spatial data handling, namely, support vector machine (SVM)-based kernel learning, semi-supervised and active learning, ensemble learning, and deep learning. These four machine learning modes are representative because they improve learning performances from different views, for example, feature space transform and decision function (SVM), optimized uses of samples (semi-supervised and active learning), and enhanced learning models and capabilities (ensemble learning and deep learning). For spatial data handling via machine learning that can be improved by the four machine learning models, three key elements are learning algorithms, training samples, and input features. To apply machine learning methods to spatial data handling successfully, a four-level strategy is suggested: experimenting and evaluating the applicability, extending the algorithms by embedding spatial properties, optimizing the parameters for better performance, and enhancing the algorithm by multiple means. Firstly, the advances of SVM are reviewed to demonstrate the merits of novel machine learning methods for spatial data, running the line from direct use and comparison with traditional classifiers, and then targeted improvements to address multiple class problems, to optimize parameters of SVM, and to use spatial and spectral features. To overcome the limits of small-size training samples, semi-supervised learning and active learning methods are then utilized to deal with insufficient labeled samples, showing the potential of learning from small-size training samples. Furthermore, considering the poor generalization capacity and instability of machine learning algorithms, ensemble learning is introduced to integrate the advantages of multiple learners and to enhance the generalization capacity. The typical research lines, including the combination of multiple classifiers, advanced ensemble classifiers, and spatial interpolation, are presented. Finally, deep learning, one of the most popular branches of machine learning, is reviewed with specific examples for scene classification and urban structural type recognition from high-resolution remote sensing images. By this review, it can be concluded that machine learning methods are very effective for spatial data handling and have wide application potential in the big data era.

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A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction

Cited by 19 publications

References 41 publications

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Advances of Four Machine Learning Methods for Spatial Data Handling: a Review

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