光学卫星图像中积雪分割精度的研究

遥感信息 Pub Date : 1997-08-03 DOI:10.1109/IGARSS.1997.615900

D. Luca, K. Seidel, M. Datcu

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引用次数: 0

摘要

对光学卫星图像中雪区分割算法的现状进行了比较。比较解决了所使用的“前向模型”的准确性和表征检测/分割算法的信息理论方面。并比较地介绍了一种新的方法:将积雪分割作为病态逆问题，并在贝叶斯推理框架下求解。

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On the accuracy of snow cover segmentation in optical satellite images

The authors make a comparison of the state of the art algorithms for snow areas segmentation in optical satellite images. The comparison address the accuracy of the "forward model" used and the informational theoretical aspects characterising the detection/segmentation algorithms. They also, comparatively, introduce and a new approach: the segmentation of the snow cover as ill-posed inverse problem and its solution in the frame of the Bayesian inference.

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遥感信息

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3984

期刊介绍： Remote Sensing Information is a bimonthly academic journal supervised by the Ministry of Natural Resources of the People's Republic of China and sponsored by China Academy of Surveying and Mapping Science. Since its inception in 1986, it has been one of the authoritative journals in the field of remote sensing in China.In 2014, it was recognised as one of the first batch of national academic journals, and was awarded the honours of Core Journals of China Science Citation Database, Chinese Core Journals, and Core Journals of Science and Technology of China. The journal won the Excellence Award (First Prize) of the National Excellent Surveying, Mapping and Geographic Information Journal Award in 2011 and 2017 respectively. Remote Sensing Information is dedicated to reporting the cutting-edge theoretical and applied results of remote sensing science and technology, promoting academic exchanges at home and abroad, and promoting the application of remote sensing science and technology and industrial development. The journal adheres to the principles of openness, fairness and professionalism, abides by the anonymous review system of peer experts, and has good social credibility. The main columns include Review, Theoretical Research, Innovative Applications, Special Reports, International News, Famous Experts' Forum, Geographic National Condition Monitoring, etc., covering various fields such as surveying and mapping, forestry, agriculture, geology, meteorology, ocean, environment, national defence and so on. Remote Sensing Information aims to provide a high-level academic exchange platform for experts and scholars in the field of remote sensing at home and abroad, to enhance academic influence, and to play a role in promoting and supporting the protection of natural resources, green technology innovation, and the construction of ecological civilisation.