Noise estimation of hyperspectral image in the spatial and spectral dimensions

Zhang, Shuo; Sun, Bin; Li, S.; Kang, Xudong

doi:10.11834/jrs.20210337

National Remote Sensing Bulletin

2021

DOI: 10.11834/jrs.20210337

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Noise estimation of hyperspectral image in the spatial and spectral dimensions

Shuo Zhang¹,

Bin Sun²,

S. Li³

et al.

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A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral Images Based on Pure Pixel Extraction

Tian

Kan

Long

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Signal-to-noise ratio (SNR) is an important radiation characteristic parameter for remote sensing image quality assessment as well as a key performance indicator for remote sensing sensors. At present, the SNR estimation methods based on regular segmentation or continuous segmentation are generally used to obtain image SNR. However, the land cover type has a great influence on the results of the SNR estimation method using regular segmentation, especially the high spectral resolution and high spatial resolution remote sensing images obtained by the low-altitude UAV hyperspectral sensor. In addition, some land cover types are difficult to achieve continuous segmentation. In view of this limitation of the existing SNR estimation methods, a new unsupervised method for estimating SNR in UAV hyperspectral images has been developed in this paper, called pure pixel extraction and spectral decorrelation (PPESDC). By directly extracting pure pixels in the spatial dimension and combining the correlation of the spectral dimension to obtain the SNR of the hyperspectral image, this new method replaces the conventional method of improving the segmentation algorithm to improve the accuracy of SNR estimation. Additionally, the box counting method is introduced to determine the image SNR aggregation interval. The results showed that the proposed method had higher accuracy and smaller errors than the other SNR estimation methods. Besides, this method had stronger robustness, it can be used for both radiance and reflectance (atmospherically corrected) UAV hyperspectral images.

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A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral Images Based on Pure Pixel Extraction

Tian

Kan

Long

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Data-Wise Spatial Regional Consistency Re-Enhancement for Hyperspectral Image Classification

Zhou

Hao

et al. 2022

Remote Sensing

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Effectively using rich spatial and spectral information is the core issue of hyperspectral image (HSI) classification. The recently proposed Diverse Region-based Convolutional Neural Network (DRCNN) achieves good results by weighted averaging the features extracted from several predefined regions, thus exploring the use of spatial consistency to some extent. However, such feature-wise spatial regional consistency enhancement does not effectively address the issue of wrong classifications at the edge of regions, especially when the edge is winding and rough. To improve the feature-wise approach, Data-wise spAtial regioNal Consistency re-Enhancement (“DANCE”) is proposed. Firstly, the HSIs are decomposed once using the Spectral Graph Wavelet (SGW) to enhance the intra-class correlation. Then, the image components in different frequency domains obtained from the weight map are filtered using a Gaussian filter to “debur” the non-smooth region edge. Next, the reconstructed image is obtained based on all filtered frequency domain components using inverse SGW transform. Finally, a DRCNN is used for further feature extraction and classification. Experimental results show that the proposed method achieves the goal of pixel level re-enhancement with image spatial consistency, and can effectively improve not only the performance of the DRCNN, but also that of other feature-wise approaches.

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Noise estimation of hyperspectral image in the spatial and spectral dimensions

Cited by 2 publications

References 0 publications

A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral Images Based on Pure Pixel Extraction

A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral Images Based on Pure Pixel Extraction

Data-Wise Spatial Regional Consistency Re-Enhancement for Hyperspectral Image Classification

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