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Abstract
Like natural images, remote sensing scene images; of which the quality represents the imaging performance of the remote sensor, also suffer from the degradation caused by imaging system. However, current methods measuring the imaging performance in engineering applications require for particular image patterns and lack generality. Therefore, a more universal approach is demanded to assess the imaging performance of remote sensor without constraints of land cover. Due to the fact that existing general-purpose blind image quality assessment (BIQA) methods cannot obtain satisfying results on remote sensing scene images; in this work, we propose a BIQA model of improved performance for natural images as well as remote sensing scene images namely BM-IQE. We employ a novel block-matching strategy called Structural Similarity Block-Matching (SSIM-BM) to match and group similar image patches. In this way, the potential local information among different patches can get expressed; thus, the validity of natural scene statistics (NSS) feature modeling is enhanced. At the same time, we introduce several features to better characterize and express remote sensing images. The NSS features are extracted from each group and the feature vectors are then fitted to a multivariate Gaussian (MVG) model. This MVG model is therefore used against a reference MVG model learned from a corpus of high-quality natural images to produce a basic quality estimation of each patch (centroid of each group). The further quality estimation of each patch is obtained by weighting averaging of its similar patches’ basic quality estimations. The overall quality score of the test image is then computed through average pooling of the patch estimations. Extensive experiments demonstrate that the proposed BM-IQE method can not only outperforms other BIQA methods on remote sensing scene image datasets but also achieve competitive performance on general-purpose natural image datasets as compared to existing state-of-the-art FR/NR-IQA methods.
Highlights
Real-time monitoring the performance of imaging equipment is important in practical applications such as environmental monitoring and resources exploration [1,2]
It shows that the four GLCM derivatives can keep great consistency and linearity under varying degradations, which suggests that the four statistics are consistency and linearity under varying degradations, which suggests that the four statistics are useful useful and effective for image quality prediction tasks
The Spearman Rank-Order Correlation Coefficient (SROCC) metric is computed between the objective scores predicted by blind image quality assessment (BIQA) algorithms and the subjective mean opinion scores (MOS) provided by images database and is generally used to evaluate the prediction monotonicity and accuracy
Summary
Real-time monitoring the performance of imaging equipment is important in practical applications such as environmental monitoring and resources exploration [1,2]. Current solutions for evaluating visual perception effect which influences the subsequent remote sensing image interpretation. In the Target method, not all on-orbit space remote sensors can evaluating the performance of remote sensors include the Target method [3], the Knife-edge method [4], obtain target images; the Knife-edge method and the. At it is urgent to develop a universal method to evaluate the performance imaging performance remote sensors present, it lacks normative approaches to assess the imaging of remote of sensors. Urgent to develop a universal method to evaluate the imaging performance of remote sensors via RS-IQA.
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