Interpretation Theory and Application Method Development for Information Extraction from High Resolution Remotely Sensed Data

Under the constant advancement of remote sensing technology,more and more high spatial and spectral resolution and high time frequency data are becoming available.The spatial resolution of satellite data is approaching 0.6m while for aerial imagery it is better than 0.1m.The spectral resolution of data can be as high as 3—4nm.These developments not only greatly increased our capability and accuracy in information extraction and monitoring but also opened new application opportunities.Existing research activities have focused on how to increase the utilization efficiency of high resolution data,particularly for high spatial resolution data in a wide range of applications such as urban environment,precision agriculture,transportation and road infrastructure,forest inventory,artificial targets recognition and disaster risk assessment.However,the overall level of automation is still low.In this paper,we introduce some of the bottleneck problems and research questions related to information extraction from high spatial resolution imagery,and high spectral resolution and polar imagery,data fusion,and high spatial resolution image change detection.We suggest that an image base and a corresponding relevant knowledge base be built to improve data sharing and to facilitate investigations in this field.