No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

Jingxiang Yang,Jonathan Cheung-Wai Chan,Chen Yi,Yongqiang Zhao

doi:10.3390/rs9040305

Jingxiang Yang, Jonathan Cheung-Wai Chan + Show 2 more

Open Access

https://doi.org/10.3390/rs9040305

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Journal: Remote Sensing	Publication Date: Mar 23, 2017
Citations: 74	License type: CC BY 4.0

Affiliation: Northwestern Polytechnical University

Abstract

Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we propose a no-reference hyperspectral image quality assessment method based on quality-sensitive features extraction. Difference of statistical properties between pristine and distorted HSIs is analyzed in both spectral and spatial domains, then multiple statistics features that are sensitive to image quality are extracted. By combining all these statistics features, we learn a multivariate Gaussian (MVG) model as benchmark from the pristine hyperspectral datasets. In order to assess the quality of a reconstructed HSI, we partition it into different local blocks and fit a MVG model on each block. A modified Bhattacharyya distance between the MVG model of each reconstructed HSI block and the benchmark MVG model is computed to measure the quality. The final quality score is obtained by average pooling over all the blocks. We assess five state-of-the-art super-resolution methods on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Hyperspec-VNIR-C (HyperspecVC) data using our proposed method. It is verified that the proposed quality score is consistent with current reference-based assessment indices, which demonstrates the effectiveness and potential of the proposed no-reference image quality assessment method.

Highlights

Hyperspectral image (HSI) with rich spatial and spectral information of the scene is useful in many fields such as mineral exploitation, agriculture, and environment management [1,2,3]
We train the benchmark multivariate Gaussian (MVG) model on HyperspecVC data, with the trained model, we evaluate the reconstructed images from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data
We propose a no-reference quality assessment method to assess reconstructed hyperspectral image (HSI)

Summary

Introduction

Hyperspectral image (HSI) with rich spatial and spectral information of the scene is useful in many fields such as mineral exploitation, agriculture, and environment management [1,2,3]. In order to evaluate the reconstructed high resolution HSI, conventional strategy is to degrade the original data into a coarser resolution by down-sampling. The original data are used as reference image and compared with the reconstructed high resolution image. The disadvantage is that as the invariance of the super-resolution performance to scale changes cannot be guaranteed, the performance of super-resolution method on the original data may not be as good as on the down-sampled data [13,14]. While it is naturally better to assess the super-resolution method on the original data rather than on the down-sampled data, reference image is not available for assessment if the super-resolution is applied on the original data

Results

Discussion

Conclusion