Hyperspectral Band Selection Using Improved Classification Map

Cao, Xin; Wei, Cuicui; Han, Jungong; Jiao, Licheng

doi:10.1109/lgrs.2017.2755541

Cited by 48 publications

(25 citation statements)

References 26 publications

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“…The third competitor (Cao et al 2017a) is a semi-supervised method that uses Image Processing tools in its wrapper-based band selection framework. Firstly, it trains a SVM classifier based on labeled instances, then this classifier assigns class label to unlabeled data, which end up having wrong labels -or pseudo ground-truth.…”

Section: Image Processing-based Approachmentioning

confidence: 99%

“…What makes them different is that multispectral sensors have spaced bands in the spectral range, whereas in HSI sensor technology, the bands are contiguous, providing finer details about the scene under analysis (Schowengerdt 2006). However, contiguous bands tend to be highly correlated, and this creates a large amount of redundant information (Cao et al 2017a). Moreover, in feature spaces with high dimensionality -resulting from numerous spectral bands-the data points become sparse, which impairs the ability of the classifier to generalize when insufficient training data are provided (Cover 1965;Theodoridis and Koutroumbas 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Another popular approach for dimensionality reduction is feature selection, which, in the realm of hyperspectral images, can also be called band selection (BS) (Cao et al 2017a. Like feature selection, BS seeks to reduce the dimensionality of the original data while retaining as much information as possible.…”

Section: Introductionmentioning

confidence: 99%

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Supervised band selection in hyperspectral images using single-layer neural networks

Habermann

Frémont

Shiguemori

2018

International Journal of Remote Sensing

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Hyperspectral images provide fine details of the scene under analysis in terms of spectral information. This is due to the presence of contiguous bands that make possible to distinguish different objects even when they have similar colour and shape. However, neighbouring bands are highly correlated, and, besides, the high dimensionality of hyperspectral images brings a heavy burden on processing and also may cause the Hughes phenomenon. It is therefore advisable to make a band selection pre-processing prior to the classification task. Thus, this paper proposes a new supervised filter-based approach for band selection based on neural networks. For each class of the data set, a binary single-layer neural network classifier performs a classification between that class and the remainder of the data. After that, the bands related to the biggest and smallest weights are selected, so, the band selection process is class-oriented. This process iterates until the previously defined number of bands is achieved. A comparison with three state-of-the-art band selection approaches shows that the proposed method yields the best results in 43.33% of the cases even with greatly reduced training data size, whereas the competitors have achieved between 13.33% and 23.33% on the Botswana, KSC and Indian Pines datasets.

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Section: Image Processing-based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervised band selection in hyperspectral images using single-layer neural networks

Habermann

Frémont

Shiguemori

2018

International Journal of Remote Sensing

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“…According to the involvement of the labeled and the unlabeled samples, band selection can be divided into supervised [9][10][11][12][13], semi-supervised [14][15][16][17] and unsupervised [18][19][20][21][22][23] methods. Supervised and semi-supervised methods utilize the labeled samples to guide the selection process.…”

Section: Introductionmentioning

confidence: 99%

Optimal Clustering Framework for Hyperspectral Band Selection

Wang

Zhang

2018

IEEE Trans. Geosci. Remote Sensing

237

132

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Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets.

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“…Traditional feature selection techniques are typically grouped into three approaches namely; filter, embedded and wrapper methods [15]. In earth observation related studies, feature selection algorithms have generally been compared based on this grouping [5,16].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

et al. 2019

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In the recent past, the volume of spatial datasets has significantly increased. This is attributed to, among other factors, higher sensor temporal resolutions of the recently launched satellites. The increased data, combined with the computation and possible derivation of a large number of indices, may lead to high multi-collinearity and redundant features that compromise the performance of classifiers. Using dimension reduction algorithms, a subset of these features can be selected, hence increasing their predictive potential. In this regard, an investigation into the application of feature selection techniques on multi-temporal multispectral datasets such as Sentinel-2 is valuable in vegetation mapping. In this study, ten feature selection methods belonging to five groups (Similarity-based, statistical-based, Sparse learning based, Information theoretical based, and wrappers methods) were compared based on f-score and data size for mapping a landscape infested by the Parthenium weed (Parthenium hysterophorus). Overall, results showed that ReliefF (a Similarity-based approach) was the best performing feature selection method as demonstrated by the high f-score values of Parthenium weed and a small size of optimal features selected. Although svm-b (a wrapper method) yielded the highest accuracies, the size of optimal subset of selected features was quite large. Results also showed that data size affects the performance of feature selection algorithms, except for statistically-based methods such as Gini-index and F-score and svm-b. Findings in this study provide a guidance on the application of feature selection methods for accurate mapping of invasive plant species in general and Parthenium weed, in particular, using new multispectral imagery with high temporal resolution.

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Hyperspectral Band Selection Using Improved Classification Map

Cited by 48 publications

References 26 publications

Supervised band selection in hyperspectral images using single-layer neural networks

Supervised band selection in hyperspectral images using single-layer neural networks

Optimal Clustering Framework for Hyperspectral Band Selection

Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

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