Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank

Bau, Tien C.; Sarkar, Subhadip; Healey, Glenn

doi:10.1109/tgrs.2010.2046494

Cited by 136 publications

(51 citation statements)

References 25 publications

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“…Besides local texture features, recent literature has reported that global spatial features of HSI data will also contribute to the classification accuracy, e.g., Gabor filter [47,56,57]. Suppose (x, y) is a pixel coordinate at I p , then the output of an Gabor filter can be expressed by…”

Section: Gabor Filtersmentioning

confidence: 99%

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

Pan

Shi

et al. 2017

Remote Sensing

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Abstract:Integrating spectral and spatial information is proved effective in improving the accuracy of hyperspectral imagery classification. In recent studies, two kinds of approaches are widely investigated: (1) developing a multiple feature fusion (MFF) strategy; and (2) designing a powerful spectral-spatial feature extraction (FE) algorithm. In this paper, we combine the advantages of MFF and FE, and propose an ensemble based feature representation method for hyperspectral imagery classification, which aims at generating a hierarchical feature representation for the original hyperspectral data. The proposed method is composed of three cascaded layers: firstly, multiple features, including local, global and spectral, are extracted from the hyperspectral data. Next, a new hashing based feature representation method is proposed and conducted on the features obtained in the first layer. Finally, a simple but efficient extreme learning machine classifier is employed to get the classification results. To some extent, the proposed method is a combination of MFF and FE: instead of feature fusion or single feature extraction, we use an ensemble strategy to provide a hierarchical feature representation for the hyperspectral data. In the experiments, we select two popular and one challenging hyperspectral data sets for evaluation, and six recently proposed methods are compared. The proposed method achieves respectively 89.55%, 99.36% and 77.90% overall accuracies in the three data sets with 20 training samples per class. The results prove that the performance of the proposed method is superior to some MFF and FE based ones.

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Section: Gabor Filtersmentioning

confidence: 99%

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

Pan

Shi

et al. 2017

Remote Sensing

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“…Recently 3-D Gabor filters have been used in this field as they have the ability to extract joint spatial and spectrum information. In 2010, Bau et al developed a model for spectral/spatial based on 3-D Gabor filters [10]. The dimensions extracted by 3-D Gabor filter were very huge.…”

Section: Gabor Filtersmentioning

confidence: 99%

A Survey on Trends and Techniques used in Hyperspectral Image Processing

Sarmah¹,

Kalita²

2015

IJCA

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Recent advancement in remote sensing facilitates collection of hyperspectral images (HSIs) in hundreds of bands which provides a potential platform to detect and identify the unique trends in land and atmospheric datasets with high accuracy. But along with the detailed information, HSIs also pose several processing problems such as-1) increase in computational complexity due to high dimensionality. So dimension reduction without losing information is one of the major concerns in this area and 2) limited availability of labeled training sets causes the ill posed problem which is needed to be addressed by the classification algorithms.Initially classification techniques of HSIs were based on spectral information only. Gradually researchers started utilizing both spectral and spatial information to increase classification accuracy. Also the classification algorithms have evolved from supervised to semi supervised mode. This paper presents a survey about the techniques available in the field of HSI processing to provide a seminal view of how the field of HSI analysis has evolved over the last few decades and also provides a snapshot of the state of the art techniques used in this area.

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“…There are two major categories utilizing spatial features: to extract some type of spatial features (e.g., texture, morphological profiles, and wavelet features), and to directly use pixels in a small neighborhood for joint classification assuming that these pixels usually share the same class membership. In the first category (feature dimensionality increased), Gabor features have been successfully used for hyperspectral image classification [15][16][17][18] recently due to the ability to represent useful spatial information. In [15,16], three-dimensional (3-D) Gabor filters were applied to hyperspectral images to extract 3-D Gabor features; in [17,18], two-dimensional (2-D) Gabor features were extracted in a principal component analysis (PCA)-projected subspace.…”

Section: Introductionmentioning

confidence: 99%

“…In the first category (feature dimensionality increased), Gabor features have been successfully used for hyperspectral image classification [15][16][17][18] recently due to the ability to represent useful spatial information. In [15,16], three-dimensional (3-D) Gabor filters were applied to hyperspectral images to extract 3-D Gabor features; in [17,18], two-dimensional (2-D) Gabor features were extracted in a principal component analysis (PCA)-projected subspace. In our previous work [19], a preprocessing algorithm based on multihypothesis (MH) prediction was proposed to integrate spectral and spatial information for noise-robust hyperspectral image classification, which falls into the second category (feature dimensionality not increased).…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

Chen

et al. 2014

Remote Sensing

223

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Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM) classifier. Specifically, Gabor filtering and multihypothesis (MH) prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM) and MH-prediction-based SVM in challenging small training sample size conditions. OPEN ACCESSRemote Sens. 2014, 6 5796

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Hyperspectral Region Classification Using a Three-Dimensional Gabor Filterbank

Cited by 136 publications

References 25 publications

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

A Survey on Trends and Techniques used in Hyperspectral Image Processing

Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

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