Superpixel segmentation and machine learning classification algorithm for cloud detection in remote-sensing images

Cloud detection is a fundamental yet challenging topic in remote-sensing image processing. The authors propose a method for multi-dimensional feature extraction and superpixel segmentation, and use a voting-based clustering ensemble to capture the whole target shape. In order to further identify clo...

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Veröffentlicht in:	Journal of engineering (Stevenage, England) England), 2019-10, Vol.2019 (20), p.6675-6679
Hauptverfasser:	Shi, Yueting, Wang, Weijiang, Gong, Qishu, Li, Dingyi
Format:	Artikel
Sprache:	eng
Schlagworte:	bright buildings cloud detection clouds feature extraction geophysical image processing geophysical techniques gf-1 remote-sensing images GF‐1 remote‐sensing images grey-level sub-regions high grey-level sub-regions IET International Radar Conference (IRC 2018) image classification image segmentation learning (artificial intelligence) machine learning classification algorithm multidimensional feature extraction Ostu threshold remote sensing remote-sensing image processing snow-covered lands softmax regression classifier superpixel segmentation target shape voting-based clustering ensemble
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