Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data

Gao, Lianru; Zhang, Fangfang; Sun, Xingming; Li, Shanshan; Du, Qian; Wu, Changshan

doi:10.1186/1687-6180-2013-65

Cited by 15 publications

(13 citation statements)

References 25 publications

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“…[10] extends the idea of Liu et al . [9] by computing noise over small non-overlapping sub-blocks locally, thereby further reducing the influence of spatial features.…”

Section: Theorymentioning

confidence: 96%

“…This is generally difficult to do so, due to the stochastic nature of noise, and as pointed out in [1, 6]. Several papers have been done to address this issue by considering the neighboring spatial information [7, 8], as well as jointly considering neighborhood spatial and spectral information [9, 10], leading to high complexity algorithms just for the NCM estimation.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Clustering-Based Noise Covariance Estimation for Maximum Noise Fraction

Gupta

Bajaj

2018

Communications in Computer and Information Science

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Most hyperspectral images (HSI) have important spectral features in specific combination of wave numbers or channels. Noise in these specific channels or bands can easily overwhelm these relevant spectral features. Maximum Noise Fraction (MNF) by Green et al. [1] has been extensively studied for noise removal in HSI data. The MNF transform maximizes the Signal to Noise Ratio (SNR) in feature space, thereby explicitly requiring an estimation of the HSI noise. We present two simple and efficient Noise Covariance Matrix (NCM) estimation methods as required for the MNF transform. Our NCM estimations improve the performance of HSI classification, even when ground objects are mixed. Both techniques rely on a superpixel based clustering of HSI data in the spatial domain. The novelty of our NCM’s comes from their reduced sensitivity to HSI noise distributions and interference patterns. Experiments with both simulated and real HSI data show that our methods significantly outperforms the NCM estimation in the classical MNF transform, as well as against more recent state of the art NCM estimation methods. We quantify this improvement in terms of HSI classification accuracy and superior recovery of spectral features.

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“…[10] extends the idea of Liu et al . [9] by computing noise over small non-overlapping sub-blocks locally, thereby further reducing the influence of spatial features.…”

Section: Theorymentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Efficient Clustering-Based Noise Covariance Estimation for Maximum Noise Fraction

Gupta

Bajaj

2018

Communications in Computer and Information Science

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“…However, noise estimation based on spatial information alone can be unstable and data-selective [25,51,53]. It is because hyperspectral images do not always have very high spatial resolution, and the difference between pixels may contain a significant signal instead of pure noise.…”

Section: Proposed Okmnf Methodsmentioning

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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“…Furthermore, the estimation results are unstable. Therefore, Gao et al (2011;Gao, Du, et al, 2013;Gao, Zhang, et al, 2013; introduced the optimized minimum noise fraction (OMNF) method. This method adopts spectral and spatial decorrelation (SSDC) method to estimate noise by considering the high correlation between bands.…”

Section: Open Accessmentioning

confidence: 99%

A new kernel method for hyperspectral image feature extraction

Zhao

Gao

Liao

et al. 2017

Geo-spatial Information Science

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Key laboratory of Digital earth science, institute of remote sensing and Digital earth, chinese academy of sciences, Beijing, china; b University of chinese academy of sciences, ringgold standard institution, Beijing, china; c Department of telecommunications and information processing, Ghent University, Ghent, Belgium ABSTRACTHyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for postapplications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where highdimensional data analysis is required.

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Optimized maximum noise fraction for dimensionality reduction of Chinese HJ-1A hyperspectral data

Cited by 15 publications

References 25 publications

Efficient Clustering-Based Noise Covariance Estimation for Maximum Noise Fraction

Efficient Clustering-Based Noise Covariance Estimation for Maximum Noise Fraction

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

A new kernel method for hyperspectral image feature extraction

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