Dr. Xiaotian Lu Profile

Dr. Xiaotian Lu

at Qian Xuesen Lab of Space Technology

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Publications (3)

Proceedings Article | 12 March 2021 Paper

Polarization imaging detection method based on dynamic vision sensor

Xiaotian Lu, Feng Li, Bian Xiao, Xue Yang, Lei Xin, Zhijia Liu

Proceedings Volume 11763, 117630Z (2021) https://doi.org/10.1117/12.2586190

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Proceedings Article | 14 February 2020 Paper

An improved detection and tracking method for small dim moving target based on particle filter

Nan Zhang, Feng Li, Xiaotian Lu, Xue Yang, Lei Xin

Proceedings Volume 11429, 114290U (2020) https://doi.org/10.1117/12.2539336

KEYWORDS: Particle filters, Target detection, Detection and tracking algorithms, Signal to noise ratio, Electronic filtering, Image processing, Surveillance systems

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Proceedings Article | 7 October 2019 Presentation + Paper

Finer scale mapping with super resolved GF-4 satellite images

Xue Yang, Feng Li, Lei Xin, Nan Zhang, XiaoTian Lu, Huachao Xiao

Proceedings Volume 11155, 111550A (2019) https://doi.org/10.1117/12.2532674

KEYWORDS: Image classification, Remote sensing, Satellite imaging, Image processing, Image registration, Landsat

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High-resolution (HR) remote sensing images are characterized by rich and detailed ground object information with more complex structures of the ground object which make the interference information is more difficult to process. It has always been the focus of domestic and foreign researchers that how to obtain more accurate and higher quality ground object information from these images. The GF-4, the world's first geostationary orbit with high spatial resolution remote sensing satellite, can provide high temporal resolution, large width and 50m pixel resolution of remote sensing data by using area array imaging technology. However, the GF-4 image is a medium resolution and low resolution (LR) image data with relatively vague details of ground objects and not obvious relationships between objects which limit the acquisition of the ground object information to some extent. Therefore, in this paper, we analyze the influence of various factors in the imaging process and construct an image degradation model according to the characteristics of GF-4 satellite images. We adopted the super resolved (SR) method based on Mixed sparse representations (MSR) to increase the spatial resolution of the GF-4 image by twice as much, which not only enriched the detailed information of the image, but also improved the image quality. For the results of SR of GF-4 imagery, we adopted the Maximum Likelihood Classification (MLC) method to perform image classification test and result verification. The experimental area selected in this paper is Yantai City, Shandong Province, China, the LANDSAT 8 OLI data is used as a training sample to calculate the overall accuracy and Kappa coefficient after classification. The results show that the overall accuracy of the superreconstructed result data is 40% higher than that of the source image data from GF-4, especially when the spectral characteristics of the ground objects are obviously different, the accuracy is more obvious. The Kappa coefficient increased 0.4, the extracted outline is more complete and the classification details are more refined.

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