scholarly journals Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

2017 ◽  
Vol 9 (12) ◽  
pp. 1237 ◽  
Author(s):  
Peng Fu ◽  
Xin Sun ◽  
Quansen Sun
Keyword(s):  
Image Segmentation ◽  
Hyperspectral Image ◽  
Noise Estimation ◽  
Mixed Noise

scholarly journals Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

2021 ◽  
Vol 13 (13) ◽  
pp. 2607
Author(s):  
Tianru Xue ◽  
Yueming Wang ◽  
Yuwei Chen ◽  
Jianxin Jia ◽  
Maoxing Wen ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Noise Estimation ◽  
Processing Unit ◽  
Estimation Model ◽  
Minimum Noise Fraction ◽  
Mixed Noise ◽  
Noise Fraction ◽  
Minimum Noise

Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.

Hyperspectral Image Segmentation For Paint Analysis

2021 ◽  
Author(s):  
Nathan Magro ◽  
Alexandra Bonnici ◽  
Stefania Cristina
Keyword(s):  
Image Segmentation ◽  
Hyperspectral Image ◽  
Paint Analysis

scholarly journals Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

2020 ◽  
Vol 12 (8) ◽  
pp. 1324
Author(s):  
Lei Sun ◽  
Bujin Li ◽  
Yongjian Nian
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares Method ◽  
Real Life ◽  
Principal Component ◽  
Noise Estimation ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Local Sample ◽  
Mixed Noise

HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is performed on the first principal component obtained by MNF (minimum noise fraction)-based dimensionality reduction to generate non-overlapping regions with similar pixels. Then, MLR is performed to remove the spectral correlation, and a system of linear equations with respect to noise variances is established according to the local sample statistics calculated within each superpixel. By solving the equations in terms of the least-squares method, the noise variances are determined. The experimental results show that the proposed algorithm provides more accurate local sample statistics, and yields a more accurate noise estimation than the other state-of-the-art algorithms for simulated HSIs. The results of the real-life data also verify the effectiveness of the proposed algorithm.

scholarly journals Fuzzy Based Hyperspectral Image Segmentation Using Subpixel Detection[

2014 ◽  
Vol 4 (3) ◽  
pp. 179-188
Author(s):  
Veera SenthilKumar.G ◽  
Dhivya. M ◽  
Sivasangari. R ◽  
Suganya. V
Keyword(s):  
Image Segmentation ◽  
Hyperspectral Image
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