Hyperspectral Band Selection Based on Rough Set

Patra, Swarnajyoti; Modi, Prahlad; Bruzzone, Lorenzo

doi:10.1109/tgrs.2015.2424236

Cited by 92 publications

(32 citation statements)

References 39 publications

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“…It can be directly obtained from the required data without any prior knowledge or additional information. So, when the prior knowledge of the ocean is not easy to obtain, it is much more objective to use rough set to reflect the uncertainty of marine knowledge [26].…”

Section: Remote Sensing Image Feature Model Based On Rough Setmentioning

confidence: 99%

A multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition

Huang

Chen

Wang

2019

Multimed Tools Appl

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The effective extraction of continuous features in ocean optical remote sensing image is the key to achieve the automatic detection and identification for marine vessel targets. Since many of the existing data mining algorithms can only deal with discrete attributes, it is necessary to transform the continuous features into discrete ones for adapting to these intelligent algorithms. However, most of the current discretization methods do not consider the mutual exclusion within the attribute set when selecting breakpoints, and cannot guarantee that the indiscernible relationship of information system is not destroyed. Obviously, they are not suitable for processing ocean optical remote sensing data with multiple features. Aiming at this problem, a multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition is presented in this paper. Firstly, the information equivalent model of remote sensing image is established based on the theories of information entropy and rough set. Secondly, the change extent of indiscernible relationship in the model before and after discretization is evaluated. Thirdly, multiple scans are executed for each band until the termination condition is satisfied for generating the optimal number of intervals. Finally, we carry out the simulation analysis of the high-resolution remote sensing image data collected near the coast of South China Sea. In addition, we also compare the proposed method with the current mainstream discretization algorithms. Experiments validate that the proposed method has better comprehensive performance in terms of interval number, data consistency, running time, prediction accuracy and recognition rate.

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Section: Remote Sensing Image Feature Model Based On Rough Setmentioning

confidence: 99%

A multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition

Huang

Chen

Wang

2019

Multimed Tools Appl

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“…Feature selection means selecting part of the original bands based on proper criterion [19]. Typical feature selection algorithms include multitask sparsity pursuit [20], structure-aware [21], support vector machine [22], hypergraph model [23], sparse Hilbert-Schmidt independence criterion [24] and nonhomogeneous hidden Markov chain model [25]. Different measures were used to select preferred bands, including mutual information [12], information divergence [13], variance [26] and local spatial information [27].…”

Section: Introductionmentioning

confidence: 99%

Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands

Hsieh

Kiang

2020

Sensors

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Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data.

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“…The supervised method uses label information to assess the quality of each band. In [6], rough set theory was used to compute the relevance and significance of each spectral band. Then, by defining a novel criterion, it selects the informative bands that have higher relevance and significance values.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Band Selection Using Improved Classification Map

Cao

Wei

Han

et al. 2017

IEEE Geosci. Remote Sensing Lett.

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Although it is a powerful feature selection algorithm, wrapper method is rarely used for hyperspectral band selection. Its accuracy is restricted by the number of labeled training samples and collecting such label information for hyperspectral image is time consuming and expensive. Benefited from the local smoothness of hyperspectral images, a simple yet effective semi-supervised wrapper method is proposed, where the edge preserved filtering is exploited to improve the pixel-wised classification map and this in turn can be used to assess the quality of band set. The property of the proposed method lies in using the information of abundant unlabeled samples and valued labeled samples simultaneously. The effectiveness of the proposed method is illustrated with five real hyperspectral datasets. Compared with other wrapper methods, the proposed method shows consistently better performance.

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Hyperspectral Band Selection Based on Rough Set

Cited by 92 publications

References 39 publications

A multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition

A multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition

Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands

Hyperspectral Band Selection Using Improved Classification Map

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