Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection

Wang, Wenguang; Wang, Wenhong; Liu, Hongfu

doi:10.3390/rs14051156

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…The presumed reason is that the ranking-based band selection methods are typically based on a single criterion. These findings are consistent with those of previous studies [ 46 , 47 ]. Moreover, as shown in Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 , the OA values of all methods increase with the increasing number of bands, but the rate of increase becomes progressively slow.…”

Section: Resultssupporting

confidence: 94%

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Wang

2023

Sensors

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Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs). However, most existing clustering-based band selection methods involve the clustering of original HSIs, limiting their performance because of the high dimensionality of hyperspectral bands. To tackle this problem, a novel hyperspectral band selection method termed joint learning of correlation-constrained fuzzy clustering and discriminative non-negative representation for hyperspectral band selection (CFNR) is presented. In CFNR, graph regularized non-negative matrix factorization (GNMF) and constrained fuzzy C-means (FCM) are integrated into a unified model to perform clustering on the learned feature representation of bands rather than on the original high-dimensional data. Specifically, the proposed CFNR aims to learn the discriminative non-negative representation of each band for clustering by introducing GNMF into the model of the constrained FCM and making full use of the intrinsic manifold structure of HSIs. Moreover, based on the band correlation property of HSIs, a correlation constraint, which enforces the similarity of clustering results between neighboring bands, is imposed on the membership matrix of FCM in the CFNR model to obtain clustering results that meet the needs of band selection. The alternating direction multiplier method is adopted to solve the joint optimization model. Compared with existing methods, CFNR can obtain a more informative and representative band subset, thus can improve the reliability of hyperspectral image classifications. Experimental results on five real hyperspectral datasets demonstrate that CFNR can achieve superior performance compared with several state-of-the-art methods.

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Section: Resultssupporting

confidence: 94%

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Wang

2023

Sensors

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“…The feature variables extracted by CARS-Rfrog in this paper were mainly concentrated in the near-infrared (NIR) region, with almost none in the visible band (except for the 366-370 nm range). This is consistent with the findings of previous studies [36,37]. The NIR spectrum is generated due to the vibrational energy level jumps and rotational energy level jumps in molecules.…”

Section: Discussionsupporting

confidence: 93%

Prediction of Soil Water Content Based on Hyperspectral Reflectance Combined with Competitive Adaptive Reweighted Sampling and Random Frog Feature Extraction and the Back-Propagation Artificial Neural Network Method

Chen

Lou

Tuo

et al. 2023

Water

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The soil water content (SWC) is a critical factor in agricultural production. To achieve real-time and nondestructive monitoring of the SWC, an experiment was conducted to measure the hyperspectral reflectance of soil samples with varying levels of water content. The soil samples were divided into two parts, SWC higher than field capacity (super-θf) and SWC lower than field capacity (sub-θf), and the outliers were detected by Monte Carlo cross-validation (MCCV). The raw spectra were processed using Savitzky–Golay (SG) smoothing and then the spectral feature variable of SWC was extracted by using a combination of competitive adaptive reweighted sampling (CARS) and random frog (Rfrog). Based on the extracted feature variables, an extreme learning machine (ELM), a back-propagation artificial neural network (BPANN), and a support vector machine (SVM) were used to establish the prediction model. The results showed that the accuracy of retrieving the SWC using the same model was poor, under two conditions, i.e., SWC above and below θf, mainly due to the influence of the lower accuracy of the super-θf part. The number of feature variables extracted by the sub-θf and super-θf datasets were 25 and 18, respectively, accounting for 1.85% and 1.33% of the raw spectra, and the variables were widely distributed in the NIR range. Among the models, the best results were achieved by the BPANN model for both the sub-θf and the super-θf datasets; the R2p, RMSEp, and RRMSE of the sub-θf samples were 0.941, 1.570%, and 6.685%, respectively. The R2p, RMSEp, and RRMSE of the super-θf samples were 0.764, 1.479%, and 4.205%, respectively. This study demonstrates that the CARS–Rfrog–BPANN method was reliable for the prediction of SWC.

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“…H YPERSPECTRAL remote sensing images (HSIs) usually consist of hundreds of narrow and continuous bands, and thus can provide rich spectral and spatial information of ground objects. Currently, HSIs are applied in a wide range of applications such as environmental protection [1]- [3], anomaly detection [4]- [6], land cover analysis [7], [8], image segmentation [9], and hyperspectral classification [10]- [12]. For these applications, hyperspectral classification is a vital task used to identify different land covers that have distinct spectral differences.…”

Section: Introductionmentioning

confidence: 99%

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

Ning,

Wang

2023

IEEE Access

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Hyperspectral band selection plays a key role for overcoming the curse of dimensionality in the classification of hyperspectral remote sensing images (HSIs). Recently, clustering-based band selection methods have demonstrated great potential to select informative and representative bands for hyperspectral classification tasks. However, most clustering-based methods perform clustering directly on the original high-dimensional data, which reduces their performance. To address this problem, a novel band selection method called global-local consistency constrained deep embedded clustering (GLC-DEC) is proposed in this paper. In GLC-DEC, to simultaneously learn the lowdimensional embedded representation and cluster assignments of all bands in an HSI, the stacked autoencoder is integrated with the K-means method. In addition, to reduce the adverse impact of a limited number of training samples available in HSIs, local and global consistency constraints are imposed on the embedded representation so that discriminatively consistent representation of all bands is learned. Specifically, local graph regularization and global graph regularization are introduced into the GLC-DEC model, by which the strong correlation between neighboring bands and the manifold structure of all bands are fully exploited. Based on the clustering results provided by GLC-DEC, a group of representative bands are selected by using the minimum noise method. Experimental results on two real datasets demonstrate that the proposed GLC-DEC outperformed several state-of-the-art methods.

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Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection

Cited by 4 publications

References 39 publications

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Prediction of Soil Water Content Based on Hyperspectral Reflectance Combined with Competitive Adaptive Reweighted Sampling and Random Frog Feature Extraction and the Back-Propagation Artificial Neural Network Method

Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

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