WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models

WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models

Land cover classification is one of the most fundamental tasks in the field of remote sensing. In recent years, fully supervised fully convolutional network (FCN)-based semantic segmentation models have achieved state-of-the-art performance in the semantic segmentation task. However, creating pixel-...

Bibliografiset tiedot
Päätekijät:	Wei Zhang, Ping Tang, Thomas Corpetti, Lijun Zhao
Aineistotyyppi:	Artikkeli
Kieli:	English
Julkaistu:	MDPI AG 2021-01-01
Sarja:	Remote Sensing
Aiheet:	land cover classification convolutional neural network segmentation model weakly supervised seeded region growing fully connected Conditional Random Fields
Linkit:	https://www.mdpi.com/2072-4292/13/3/394

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