Segmentation of Satellite Imagery Using Hierarchical Thresholding and Neural Networks
Segmentation of Satellite Imagery Using Hierarchical Thresholding and Neural Networks
复制标题
使用分层阈值和神经网络对卫星图像进行分割
DOI：
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发表时间：
1994
期刊：
影响因子：
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通讯作者：
P. M. Tag
中科院分区：
文献类型：
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作者：
J. E. Peak;P. M. Tag
Abstract A significant task in the automated interpretation of cloud features on satellite imagery is the segmentation of the image into separate cloud features to be identified. A new technique, hierarchical threshold segmentation (HTS), is presented. In HTS, region boundaries are defined over a range of gray-shade thresholds. The hierarchy of the spatial relationships between collocated regions from different thresholds is represented in tree form. This tree is pruned, using a neural network, such that the regions of appropriate sizes and shapes are isolated. These various regions from the pruned tree are then collected to form the final segmentation of the entire image. In segmentation testing using Geostationary Operational Environmental Satellite data, HTS selected 94% of 101 dependent sample pruning points correctly, and 93% of 105 independent sample pruning points. Using Advanced Very High Resolution Radiometer data, HTS correctly selected 90% of both the 235-case dependent sample and the 253-case ...
