Ms. Lei Xin Profile

Lei Xin

at China Academy of Space Technology

SPIE Involvement:

Author

Publications (9)

Proceedings Article | 27 March 2022 Paper

Foreground and background separation of sparse infrared image based on combining sensing matrix

Guanghui Ding, Jun Ma, Lei Xin, Feng Li

Proceedings Volume 12169, 121690E (2022) https://doi.org/10.1117/12.2619603

KEYWORDS: Infrared radiation, Infrared imaging, Target detection, Image compression, Compressed sensing, Infrared sensors, Image segmentation, Signal processing, Infrared detectors, Image processing

Read Abstract +

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

Read Abstract +

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

Read Abstract +

Proceedings Article | 7 October 2019 Paper

An onboard-camera pose estimation method based on 3D digital surface model

Xing Chang, Feng Li, Guiqin Yang, Yuhong Liu, Lei Xin, Xue Yang, Nan Zhang, Xiaoyong Wang

Proceedings Volume 11155, 111552C (2019) https://doi.org/10.1117/12.2533011

KEYWORDS: 3D modeling, Cameras, 3D image processing, Feature extraction, Imaging systems, Satellites, Remote sensing, Calibration, RGB color model, Motion models

Read Abstract +

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

Read Abstract +

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.

Proceedings Article | 24 October 2018 Paper

Destriping methods for high resolution satellite multispectral remote sensing image based on GPU adaptive partitioning technology

Xue Yang, Feng Li, Lei Xin, Cheng Wang, XiaoYong Wang, Xing Chang

Proceedings Volume 10783, 107832I (2018) https://doi.org/10.1117/12.2325311

KEYWORDS: Image quality, Image processing, Satellites, Remote sensing, Image filtering, Satellite imaging, Earth observing sensors, Image enhancement, Gaussian filters, Image analysis

Read Abstract +

Proceedings Article | 10 October 2017 Paper

High efficient optical remote sensing images acquisition for nano-satellite: reconstruction algorithms

Yang Liu, Feng Li, Lei Xin, Jie Fu, Puming Huang

Proceedings Volume 10427, 104271Y (2017) https://doi.org/10.1117/12.2278180

KEYWORDS: Remote sensing, Reconstruction algorithms, Image compression, Image acquisition, Compressed sensing, Image processing, Aerospace engineering, Associative arrays, Lithium, Data acquisition

Read Abstract +

Proceedings Article | 4 October 2017 Presentation + Paper

New developments in super-resolution for GaoFen-4

Feng Li, Jie Fu, Lei Xin, Yuhong Liu, Zhijia Liu

Proceedings Volume 10427, 1042709 (2017) https://doi.org/10.1117/12.2278158

KEYWORDS: Super resolution, Spatial resolution, Remote sensing, Image resolution, Wavelet transforms, Lawrencium, Earth observing sensors, Satellites, Image compression, Image restoration

Read Abstract +

Proceedings Article | 29 September 2017 Presentation + Paper

High efficient optical remote sensing images acquisition for nano-satellite framework

Feng Li, Lei Xin, Yang Liu, Jie Fu, Yuhong Liu, Yi Guo

Proceedings Volume 10423, 104231Q (2017) https://doi.org/10.1117/12.2278171

KEYWORDS: Image compression, Image acquisition, Remote sensing, Image processing, CMOS sensors, Image quality, CCD image sensors, Cameras, Sensors, Reconstruction algorithms

Read Abstract +

Showing 5 of 9 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years