Development status and prospects of multi-modal remote sensing stereoscopic information acquisition

Stereoscopic remote sensing information enables comprehensive and accurate characterization of the spatial and multi-dimensional attributes of observed scenes/targets, demonstrating significant application potential in fields such as crop phenotype extraction, vegetation biomass estimation, and military target reconnaissance. With the rapid advancement of remote sensing science, sensor technology, and artificial intelligence, the acquisition of stereoscopic remote sensing information has evolved into a mode characterized by multi-platform collaboration, multi-sensor fusion, and multi-dimensional data processing. However, existing reviews tend to focus on relatively singular platforms, sensors, or data processing methods, lacking a systematic exploration of the overall framework, internal correlations, and developmental logic of this multi-modal stereoscopic remote sensing information acquisition technology system. To address this gap, this paper reviews and summarizes the current developments in multi-modal stereoscopic remote sensing information acquisition from three perspectives: the sky-air-ground multi-platform collaborative detection mode, the image-spectrum-point cloud multi-sensor fusion perception mode, and the 3D reconstruction-heterogeneous registration multi-data processing paradigm. Finally, potential future research directions in multi-modal stereoscopic remote sensing information acquisition are outlined.