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Special Issue On
Multi-Source Remote Sensing Data Processing and Applications

Release Date: 2020-12-29 Visited:
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CHEOS (China High-resolution Earth Observation System) program was implemented from 2010, and this program comprises the elements of the space-borne system, the near-space system, aerial system, the ground system and application system as a whole to realize Earth Observation at high temporal, spatial and spectral resolutions. A series satellites (GaoFen-1, GaoFen-2, etc) of CHEOS were developed to realize breakthroughs focusing on high spatial resolution, high spectral resolution, high spatiotemporal resolution, optical remote sensing technique, and high-precision and high-stability attitude control technology.

With the successful delivery of high-quality satellite products, high-resolution data have been utilized in an increasing number of remote sensing applications for earth observation, and the applications are ranging from geology, hydrology, forest and agriculture, etc. Besides, multi-source collaborative observation with airborne platform, including RGB image, infrared image, terrestrial image, multispectral data, and hyperspectral data, etc, and has been widely used in land-cover classification, target detection, and recognition tasks. However, challenges remain for multi-source high-resolution remote sensing data processing, and their respective applications in remote sensing. The special issue aims to collect and highlight outstanding contributions in terms of multi-source data processing/interpretation technologies as well as their recent applications to relevant scenarios.

We invite you to submit the most recent advancements in (but not limited to) the following topics:

● Advanced multi-sensor data processing for radiometric and geometric calibration, registration, and quality assurance

● Multi-source data fusion and data representation, multi-view and stereo view 3D reconstruction based on satellite images

● Multi-source feature extraction, feature fusion, and feature interpretation.

● Supervised/weakly supervised and unsupervised classification using classical and deep learning methods based on multi-source collaboration

● Multi-source collaborative analysis with RGB image, infrared image, multispectral image, hyperspectral image, and LiDAR etc.

● Technique reviews on the related topics.

Schedule

March 31, 2021 Full paper submission deadline

June 30, 2021 Publication date

Format

● All submissions will be peer reviewed according to the Journal of Beijing Institute of Technology guidelines. The contributions should be original and have not been published or submitted elsewhere. Submit your manuscript onhttps://mc03.manuscriptcentral.com/jbit. Prospective authors should consult the sitehttp://journal.bit.edu.cn/jbitfor guidelines and information on paper submission.

● All submissions must be formatted according to the format requirements provided onhttp://journal.bit.edu.cn/jbit.

Guest Editors

Mengmeng Zhang, Beijing Institute of Technology, China (mengmengzhang@bit.edu.cn)

Lu Li, Beijing Information Science and Technology University, China (20192380@bistu.edu.cn)

Zhaokui Li, Shenyang Aerospace University, China (lzk@sau.edu.cn)

Hengchao Li, Southwest Jiaotong University, China (hcli@home.swjtu.edu.cn)

Wei Li, Beijing Institute of Technology, China (leewei36@gmail.com)

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Release Date: 2020-12-29 Visited:
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