Chinese Academy of Sciences

Researchers led by Prof. Xie Chengjun and Associate Prof. Zhang Jie at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences have developed a novel deep learning-based method for satellite imagery. Their method, called the Frequency-Adaptive Mixture of Experts Network (FAME-Net), has been accepted for publication in the 2024 Proceedings of the ...

Scientists from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS), together with collaborators from Shihezi University (Xinjiang, China) and University of Toronto (Toronto, Canada), have proposed a cotton mapping strategy and provided high-resolution cotton maps throughout Xinjiang Uygur Autonomous Region, China. Xinjiang is the leading and largest cotton production region ...

Scientists from the Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), have proposed a new algorithm called AGAR, which makes it possible to detect tree heights only using 10-cm stereoscopic imagery, according to a study published in Remote Sensing of Environment on May 10. Current knowledge on the limitation of stereoscopic imagery is ...

Both the geometric and spectral information of the target are available with the hyperspectral light detection and ranging (LiDAR) in a single measurement. This advanced technique extends the scope of imaging spectroscopy to spectral three-dimensional (3D) sensing. However, two major geometric radiative effects exist in hyperspectral LiDAR, namely, the distance effect and incidence angle effect, ...

The Chinese Academy of Sciences and Wuhan University are conducting a sky-ground scientific experiment to test the condition of glaciers in the Haibei Tibetan autonomous prefecture in Northwest Qinghai province. The experiment has applied radars in three bands — P-band, L-band and Very High Frequency — and undertaken 11 flights, among which seven carried out ...

Researchers from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS) have found that the proportion of sunlit background visible in remote sensing images controls the vertical distribution of stereoscopic point clouds. The higher the proportion of visible sunlit background, the stronger the ability to detect the understory terrain. The team ...