Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

Chen, Jike; Xia, Junshi; Du, Peijun; Chanussot, Jocelyn; Xue, Zhaohui; Xie, Xinglong

doi:10.3390/rs8070601

Cited by 16 publications

(6 citation statements)

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“…Specifically, the assumptions of stationarity and ergodicity of the system either in terms of data statistics (e.g., [1], [16], [17], and [18] chap. 3) and processing approach (e.g., [2], [19]- [21], [18] chap. 16) are implied and/or drawn in order to maximize the trade off between precision and efficiency of the remote sensing data analysis frameworks.…”

Section: A System Modelmentioning

confidence: 99%

Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection

Chlaily

Mura

Chanussot

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth's surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this paper, we aim at predicting the maximum information extraction that can be reached when analyzing a given dataset. By means of an asymptotic information theory-based approach, we investigate the reliability and accuracy that can be achieved under optimal conditions for multimodal analysis as a function of data statistics and parameters that characterize the multimodal scenario to be addressed. Our approach leads to the definition of two indices that can be easily computed before the actual processing takes place. Moreover, we report in this paper how they can be used for operational use in terms of image selection in order to maximize the robustness of the multimodal analysis, as well as to properly design data collection campaigns for understanding and quantifying physical phenomena. Experimental results show the consistency of our approach.

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Section: A System Modelmentioning

confidence: 99%

Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection

Chlaily

Mura

Chanussot

et al. 2021

IEEE Trans. Geosci. Remote Sensing

Self Cite

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“…Moreover, the combination of an arbitrary number of classifiers performed at each iteration makes AdaBoost typically robust to overfitting (even after a large number of iterations), as well as capable of reducing the generalization error. For these reasons, AdaBoost has been successfully employed in several research fields, from face recognition to biomedical imaging, from natural language processing to remote sensing data analysis [20], [23], [27].…”

Section: A Main Approaches In Model-level Ensemble Learningmentioning

confidence: 99%

“…In this way, we can provide a shared platform for the aforementioned schemes of dimensionality reduction so that consistent evaluation of the performance can be conducted. When RF is employed, the number of decision trees was empirically set to 100 decision trees, and √ F 2 variables are randomly drawn at each node of the trees, where F is the number of features associated with each pixel that has been identified by the aforementioned selection architectures [27]. The SVM setup is analogous to the one we used for our proposed approach.…”

Section: ) Ensemble Learning At Model Levelmentioning

confidence: 99%

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Marinoni,

Chlaily,

Khachatrian

et al. 2021

Preprint

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Modern data analytics take advantage of ensemble learning and transfer learning approaches to tackle some of the most relevant issues in data analysis, such as lack of labeled data to use to train the analysis models, sparsity of the information, and unbalanced distributions of the records. Nonetheless, when applied to multimodal datasets (i.e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations. In fact, in multimodal data analysis, not all observations would show the same level of reliability or information quality, nor an homogeneous distribution of errors and uncertainties. This condition might undermine the classic assumptions ensemble learning and transfer learning methods rely on. In this work, we propose an adaptive approach for dimensionality reduction to overcome this issue. By means of a graph theory-based approach, the most relevant features across variable size subsets of the considered datasets are identified. This information is then used to set-up ensemble learning and transfer learning architectures. We test our approach on multimodal datasets acquired in diverse research fields (remote sensing, brain-computer interfaces, photovoltaic energy). Experimental results show the validity and the robustness of our approach, able to outperform state-of-the-art techniques.

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“…Some examples are non-parametric weighted feature extraction [15], dimension reduction techniques [16], attraction points feature extraction [17], band clustering [18], etc. In addition, other researchers focus on designing more robust classifiers, such as classification methods based on sparse representation [19] and ensemble learning [20][21][22][23]. In later research, HIC-SS methods based on deep models gradually became mainstream.…”

Section: Introductionmentioning

confidence: 99%

Advances in Hyperspectral Image Classification Methods with Small Samples: A Review

Wang,

Liu,

Chi

et al. 2023

Remote Sensing

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Hyperspectral image (HSI) classification is one of the hotspots in remote sensing, and many methods have been continuously proposed in recent years. However, it is still challenging to achieve high accuracy classification in applications. One of the main reasons is the lack of labeled data. Due to the limitation of spatial resolution, manual labeling of HSI data is time-consuming and costly, so it is difficult to obtain a large amount of labeled data. In such a situation, many researchers turn their attention to the study of HSI classification with small samples. Focusing on this topic, this paper provides a systematic review of the research progress in recent years. Specifically, this paper contains three aspects. First, considering that the taxonomy used in previous review articles is not well-developed and confuses the reader, we propose a novel taxonomy based on the form of data utilization. This taxonomy provides a more accurate and comprehensive framework for categorizing the various approaches. Then, using the proposed taxonomy as a guideline, we analyze and summarize the existing methods, especially the latest research results (both deep and non-deep models) that were not included in the previous reviews, so that readers can understand the latest progress more clearly. Finally, we conduct several sets of experiments and present our opinions on current problems and future directions.

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Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples

Cited by 16 publications

References 50 publications

Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection

Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection

Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

Advances in Hyperspectral Image Classification Methods with Small Samples: A Review

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