Abstract
Different from natural image quality assessment methods, satellite stereo images have different requirements on quality in different application scenarios, which poses a huge challenge to establish a suitable objective evaluation model. In this paper, we focus on the quality evaluation of high resolution panchromatic (satellite stereo) images in specific application scenarios of building detection. First, we build a new satellite stereo image database (SSID), which consists of 400 distorted source satellite stereo images (SSIs) generated from the 20-source SSIs with two distortion types and 10-distortion strengths. We use detection accuracy scores to represent the quality of the SSIs, which is obtained through building detection, not subjective testing. We then propose an objective evaluation model based on joint dictionary learning. In the training phase, we bridge the features of the SSIs and the corresponding detection accuracy scores through joint dictionary learning. In the testing phase, we used sparse coding to get the quality of the testing image. The experimental results demonstrate the effectiveness of the proposed method.
Highlights
In recent years, remote sensing satellite technology has been widely used in various fields, such as land resources, marine resources, agriculture, forestry, water conservancy, seismic monitoring and environment, which has brought enormous economic and social benefits [1]
With the successful launch of high-resolution remote sensing satellites such as ZY-3, and GF-1 and WorldView-3 [2], [3] remote sensing satellite data plays a key role in the reconstruction of earth surface information, such as maps provided by Google Earth and Baidu
In this paper, we focus on the quality evaluation of high resolution panchromatic image (i.e., satellite stereo images (SSIs)) in a specific application scenario for building detection
Summary
Remote sensing satellite technology has been widely used in various fields, such as land resources, marine resources, agriculture, forestry, water conservancy, seismic monitoring and environment, which has brought enormous economic and social benefits [1]. With the successful launch of high-resolution remote sensing satellites such as ZY-3, and GF-1 and WorldView-3 [2], [3] remote sensing satellite data plays a key role in the reconstruction of earth surface information, such as maps provided by Google Earth and Baidu. Due to the complexity of the imaging process of remote sensing images, images are inevitably subject to external and internal interference during the formation, transmission and reception, resulting in a certain degree of noise in the image. These noises and blurs degrade the image, and the features are submerged, which makes the understanding of the image difficult [8]. It is necessary to establish an efficient satellite image quality assessment model to select better quality remote sensing images
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