Currently, more and more earth observation data have been acquired by many kinds of sensors on different platform, such as optic sensors, microwave sensors, infrared sensors, hyperspectral sensors, etc. Thanks to giant resource being required to store and transmit these tremendous data so that the cost is very large and the efficiency is low, investigators are compelled to process them on-board as possible as they can. So far, on-board data processing only settles on some simple preprocessing, such as correction, denoising, compensation, etc. Information extraction not only is the objective of earth observation, but can distill large amount data so that amount of data needing to be stored and transmitted is reduced greatly. Feature extraction, change detection, and object recognition executed on-board will provide us an efficient information extraction system for earth observation. Data fusion technique has been widely used to process earth observation data on the ground, which can generate data with higher quality and extract better information from multisource or multitemporal data. Furthermore, data fusion can also be used to extract better information from these data on-board, simultaneously, the redundant data will be eliminated greatly so as to accelerate data processing and reduce data for storage and transmission. However, on-board data fusion processing will confront more difficulty, one of the most principal troubles is that on-board data processing system must be completely autonomous, which results in some procedures such as image registration, feature extraction, change detection, object recognition becoming more complicated, while they can be processed by help of manual operates despite being difficult on the ground. Of course, the tremendous advantage of data fusion for on-board data processing will promote investigators to remove the obstacles on the road to on-board data fusion-based information extraction
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