Spectral Embedded Adaptive Neighbors Clustering

Wang, Qi; Qin, Zequn; Nie, Feiping; Li, Xuelong

doi:10.1109/tnnls.2018.2861209

Cited by 137 publications

(33 citation statements)

References 18 publications

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“…Thus, the overall complexity of the algorithm scales linearly with the number of ALS iterations and with the tensor ranks, and cubically in the problem dimensions. When processing large datasets, the extra complexity could be partially mitigated by applying image segmentation or band selection [6], [37], [38] strategies. This analysis is beyond the scope of the present work and will be addressed in the future.…”

Section: E Computational Complexity Of Algorithmmentioning

confidence: 99%

Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Imbiriba

Borsoi

Bermudez

2020

IEEE Trans. Geosci. Remote Sensing

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Traditional hyperspectral unmixing methods neglect the underlying variability of spectral signatures often observed in typical hyperspectral images (HI), propagating these missmodeling errors throughout the whole unmixing process. Attempts to model material spectra as members of sets or as random variables tend to lead to severely ill-posed unmixing problems. Although parametric models have been proposed to overcome this drawback by handling endmember variability through generalizations of the mixing model, the success of these techniques depend on employing appropriate regularization strategies. Moreover, the existing approaches fail to adequately explore the natural multidimensinal representation of HIs. Recently, tensor-based strategies considered low-rank decompositions of hyperspectral images as an alternative to impose low-dimensional structures on the solutions of standard and multitemporal unmixing problems . These strategies, however, present two main drawbacks: 1) they confine the solutions to low-rank tensors, which often cannot represent the complexity of real-world scenarios; and 2) they lack guarantees that endmembers and abundances will be correctly factorized in their respective tensors. In this work, we propose a more flexible approach, called ULTRA-V, that imposes low-rank structures through regularizations whose strictness is controlled by scalar parameters. Simulations attest the superior accuracy of the method when compared with state-of-the-art unmixing algorithms that account for spectral variability.

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Section: E Computational Complexity Of Algorithmmentioning

confidence: 99%

Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Imbiriba

Borsoi

Bermudez

2020

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…The number of divided blocks K deciding the dimension of extracted feature is chosen from the set of {3, 5,10,15,20,25,30,35,40, 50} in sequence. Figure 10 presents the experimental result.…”

Section: Parameters Setting Discussionmentioning

confidence: 99%

“…These spectra are represented by hundreds of continuous bands that can meticulously describe the characteristics of different materials to recognize their subtle differences [3]. Therefore, owing to this good discriminative property of hyperspectral image, it has been widely used in many remote sensing research fields [4,5], such as image denoising [6,7], hyperspectral unmixing [8,9], band selection [10,11], target detection [12,13], and image classification [14,15]. They all have important practical applications in geological exploration, urban remote sensing and planning management, environment and disaster monitoring, precision agriculture, archaeology, etc.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

Yuan

Wang

2019

Remote Sensing

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A hyperspectral image usually covers a large scale of ground scene, which contains various materials with different spectral properties. When directly exploring the background information using all the image pixels, complex spectral interactions and inter-/intra-difference of different samples will significantly reduce the accuracy of background evaluation and further affect the detection performance. To address this problem, this paper proposes a novel hyperspectral anomaly detection method based on separability-aware sample cascade model. Through identifying separability of hyperspectral pixels, background samples are sifted out layer-by-layer according to their separable degrees from anomalies, which can ensure the accuracy and distinctiveness of background representation. First, as spatial structure is beneficial for recognizing target, a new spectral–spatial feature extraction technique is used in this work based on the PCA technique and edge-preserving filtering. Second, depending on different separability computed by sparse representation, samples are separated into different sets which can effectively and completely reflect various characteristics of background across all the cascade layers. Meanwhile, some potential abnormal targets are removed at each selection step to avoid their effects on subsequent layers. Finally, comprehensively taking different good properties of all the separability-aware layers into consideration, a simple multilayer anomaly detection strategy is adopted to obtain the final detection map. Extensive experimental results on five real-world hyperspectral images demonstrate our method’s superior performance. Compared with seven representative anomaly detection methods, our method improves the average detection accuracy with great advantages.

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“…Hence, it is essential to reduce hyperspectral data. Feature selection has been a hot topic in the field of machine learning [2][3][4], which is viewed as an effective measure in HSI analysis for dimensionality reduction. It removes some redundant information and can also obtain satisfactory results in comparison to the raw data.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor

Wang

2019

Remote Sensing

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A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small.

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Spectral Embedded Adaptive Neighbors Clustering

Cited by 137 publications

References 18 publications

Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor

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