In this paper, we reported the current result on the development of a cloud segmentation strategy for multispectral imageries (MSI) captured by LAPAN-A2 satellite. The segmentation was performed by involving 3200 sets of images using both deep-learning and classical-based approaches. For the deep-learning side, the U-Net was considered since it is one of the state-of-the-art image segmentation methods. On the other side, HSV (Hue, Saturation, Value) color space-based segmentation was chosen for the classical-based method. The performance of both approaches has been evaluated and compared in terms of their accuracy and speed. The comparison results provided in this paper could be used as a reference in choosing a proper strategy to extract cloud blobs existing on LAPAN-A2 MSI.


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    Title :

    Cloud Segmentation Strategy for LAPAN-A2 Multispectral Imagery


    Contributors:


    Publication date :

    2021-11-03


    Size :

    4323781 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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