Spectral clustering and spatial Frobenius norm‐based Jaya optimisation for BS of hyperspectral images

Patro, Ram Narayan; Subudhi, Subhashree; Biswal, Pradyut Kumar

doi:10.1049/iet-ipr.2018.5109

Cited by 21 publications

(8 citation statements)

References 30 publications

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“…Generally, HSI lessening can well be attained by feature extraction or feature selection (also termed as band selection) techniques . Several approaches of BS can be categorized into supervised (Patro, Subudhi, et al, 2019a) (Yang, Du, Su, & Sheng, 2010) semi-supervised as well as unsupervised (Patra et al 2015) techniques. Again, depending on the type of information used, the process can be spectral (Patro, Subudhi, et al, 2019b) or spatial.…”

Section: Introductionmentioning

confidence: 99%

Spatial residual clustering and entropy based ranking for hyperspectral band selection

Raju

Varma²,

Lakshmi

2019

European Journal of Remote Sensing

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Though the Hyper-spectral images (HSI) are associated with rich spectral information for discriminating the class-specific objects, the high dimensional data generates Hughes effect for additional processing. So, during pre-processing, band Selection (BS) is done to lower the dimension. The proposed unsupervised BS technique follows '3ʹ subsequent steps, such as 1) spatial residual (Gaussian) filtering, 2) spatial feature-based band clustering (using K-means) and 3) Entropy-based ranking. Initially, the bands are filtered and the self residual images are obtained. The filtered residual images are mapped to k clusters, where each cluster represents unique spatial information. Further, each cluster undergoes selection through the informationtheoretic (Entropy) approach. Also, from the selected optimal bands, SpatioSpectral information is extracted to appraise the performance using Support Vector Machine (SVM) classifiers with different state-of-art approaches. Performance measures like Average Accuracy (AA), Kappa (κ) and Overall Accuracy (OA) are contrasted and are presented in tables. The experiment shows propitious results as contrasted to other approaches, and suggests that pre clustering of the bands is informative as the adjacent bands are strongly correlated. Also, on comparing other approaches, the proposed one is computationally cheap and faster, which may best suit for online (in-camera) BS purposes.

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Section: Introductionmentioning

confidence: 99%

Spatial residual clustering and entropy based ranking for hyperspectral band selection

Raju

Varma²,

Lakshmi

2019

European Journal of Remote Sensing

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“…Four papers are presented for band selection of HSI [13–16]. Specifically, Zhang et al propose a crossover‐based gravitational search algorithm to identify and select the most informative subset of band [13].…”

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confidence: 99%

“…Chen et al propose an approach to combine band selection and feature extraction for dimension reduction of HSI [15], where determinantal point process is exploited for band selection followed by two‐dimensional singular spectrum analysis for feature extraction on the selected bands. Patro et al propose a hybrid approach for band selection by combining spectral clustering and Frobenius norm‐based Jaya optimisation [16], where both spectral and spatial information are considered.…”

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confidence: 99%

“…Meanwhile, the sparse representation and sparse learning have also be emphasised in data classification [10, 11], using kernel joint sparse representation and sparse dictionary learning, following the success in [24, 25]. Another trend is the combination of various optimisation algorithms to solve the optimisation problems, such as gravitational search algorithm [13] and Jaya optimisation [16] for band selection and genetic algorithm for target detection [18]. For effective feature extraction, one trend is the feature transforms.…”

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confidence: 99%

“…Not surprisingly, it continues the trend of data fusion in HSI, including spectral‐spatial fusion [8–10, 12, 16, 17] and multiple data sources fusion [21]. It is worth noting the saliency based approach in [17], where spatial saliency and spectral saliency are combined for effective target detection, is an extension of conventional saliency detection in computer vision [29, 30].…”

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Spectral clustering and spatial Frobenius norm‐based Jaya optimisation for BS of hyperspectral images

Cited by 21 publications

References 30 publications

Spatial residual clustering and entropy based ranking for hyperspectral band selection

Spatial residual clustering and entropy based ranking for hyperspectral band selection

Guest Editorial: Hyperspectral Imaging and Applications

Advancements in Multiobjective Hyperparameterization Optimization: A Comprehensive Review

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