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Evolution and evaluation of a trainable cloud classifier
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PRE
AI
DOI：10.1109/36.628797.png)
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Abstract
En 中文
Neural network classifiers have recently been popular in image classification and remote sensing applications, In this paper a case study is reported, where the evolution started with a pure neural network based solution and reached a simplified classifier with a few neural network properties, This seems to be a typical evolution concerning neural networks, A multispectral cloud classifier was implemented to automate the interpretation of AVHRR (Advanced Very High Resolution Radiometer) images, It can be adapted to changing situations with new examples. This is a requirement in satellite image applications, hence changes in illumination, round the year, during day and night, and aging of electronics are possible, The classification is done in two phases, clouds are separated from the background and then only clouds are classified. The evaluation of the classifier is based on the comparison between the SYNOP observations and the satellite observations, Comparisons with other published results show that the classifier is working well.
Keywords:
PATTERN-RECOGNITION
NEURAL-NETWORK
COVER ANALYSIS
AVHRR IMAGERY
REGIONS
SURFACE
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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