Feature Mining for Hyperspectral Image Classification

Jia, Xiuping; Kuo, Bor‐Chen; Crawford, Melba M.

doi:10.1109/jproc.2012.2229082

Cited by 339 publications

(133 citation statements)

References 118 publications

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“…Obviously, it is not easy to be satisfied to the hyperspectral case [4]. Dimensionality reduction is a very effective technique to solve this problem [5,6]. Dimensionality reduced data should well represent the original data, and can be considered as the extracted features for classification [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

et al. 2017

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This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

et al. 2017

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“…It is also worth mentioning that only a small number of these numerous features are really informative for the classification problem at hand [9]. Therefore, feature selection and feature extraction are widely used to reduce the dimensionality of features before hyperspectral image classification [10]. Feature extraction aims to project the data into a new feature space with lower dimension than before through a mathematical transformation [11].…”

Section: Introductionmentioning

confidence: 99%

“…Feature selection falls into two categories which are the filter approach and wrapper approach. Due to its independence from the classifier, the filter approach is widely used in computer vision and pattern recognition, and aims at selecting a feature subset from the original feature set according to a selection criterion and the feature subset search algorithm [10]. Compared to feature extraction methods, feature selection methods can retain well the physical nature of features and thus the features selected have good interpretability [10,12].…”

Section: Introductionmentioning

confidence: 99%

An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

Zhao

Wang

et al. 2017

Remote Sensing

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Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral

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“…Due to the abundance of information contained in HSI, hyperspectral imaging has opened new avenues in remote sensing [1][2][3][4][5]. One of the most important tasks in HSI is pixel-oriented classification [6][7][8][9], where each pixel is labeled by one of the classes based on the training samples given for each class.…”

Section: Introductionmentioning

confidence: 99%

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Liu

2016

Remote Sensing

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Abstract:Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.

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Feature Mining for Hyperspectral Image Classification

Cited by 339 publications

References 118 publications

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

An Improved Combination of Spectral and Spatial Features for Vegetation Classification in Hyperspectral Images

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

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