Mr. WenPing Qi Profile

WenPing Qi

at China Ctr. For Resources Satellite Data and Application

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Proceedings Article | 16 February 2022 Paper

Assessment of image sharpness evaluation methods and image sharpness changes in GF-4 satellite time-series data

Yuhao Wang, Wei Yi, Yong Zeng, Wenbo Su, Wenping Qi

Proceedings Volume 12083, 120832V (2022) https://doi.org/10.1117/12.2623537

KEYWORDS: Image analysis, Satellites, Satellite imaging, Image quality, Remote sensing, Earth observing sensors, Cameras, Image processing, Statistical analysis, Spatial frequencies

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The Gaofen-4 satellite (GF-4) is the first Chinese remote sensing satellite to be placed in a geosynchronous orbit, which has a wide range of application prospects. The image sharpness evaluation method is a key step to quickly, accurately and automatically obtain the quality of a large number of GF-4 remote sensing data and understand the changes of the in-orbit satellite. Moreover, the assessment of image sharpness changes is the verification of the stability of the satellite operation, and can also provide reference for the follow-up satellite planning. Taking the time-series GF-4 panchromatic images of Zhengzhou and Chifeng from May 2016 to July 2020 as the data source, we use the block standard deviation sharpness evaluation method based on spatial domain and the power spectrum sharpness evaluation method based on frequency domain to calculate four kinds image sharpness evaluation values and carry out the correlation analysis from the same day and the same month, considering whether to remove the influence of image brightness. We analyze the influence of image brightness on the sharpness evaluation, and explores the applicability of these two methods for GF-4 image, so as to select the best sharpness evaluation method. The results show that the image power spectrum that has been normalized by the average image brightness is the most suitable image sharpness evaluation method for GF-4 image. This method is used to evaluate the image quality of 208 GF-4 panchromatic images in Zhengzhou and Chifeng. The results show that: since the delivery of GF-4 in orbit test satellite, the image sharpness is stable. At the same time, some suggestions are given for the planning of subsequent geosynchronous orbit satellites. It is necessary to improve the anti-interference ability of the camera to the atmospheric conditions, platform jitter and other external conditions.

Proceedings Article | 1 June 2021 Paper

Extracting ground object information from remote sensing images based on DeepLabv3+ model integrating dual attention mechanism

Wenbo Su, Shusong Huang, WenPing Qi, Yuhao Wang, Wei Yi

Proceedings Volume 11848, 118480P (2021) https://doi.org/10.1117/12.2600380

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Ground object information extraction is the key to remote sensing image applications. High-resolution remote sensing images contain complex feature information. However, traditional feature information extraction methods have certain accuracy limitations, while deep learning techniques largely make up for the shortcomings of traditional methods. Aimed at the slow speed and inaccurate boundary region segmentation in remote sensing image feature extraction by the DeepLabv3+ model, this paper introduces an attention mechanism, embedding the spatial and channel attention mechanism modules in the feature extraction network. The combined model was tested on the ISPRS remote sensing dataset and achieved 78.68% accuracy. The results show that the proposed network structure is capable of generalization and is feasible in ground object information extraction from high-resolution remote sensing images.

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