Please use this identifier to cite or link to this item:
Title: CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images
Authors: Kanu S.
Khoja R.
Lal S.
Raghavendra B.S.
Issue Date: 2020
Citation: Remote Sensing Applications: Society and Environment , Vol. 20 , , p. -
Abstract: Cloud Detection is an important pre-processing step for any application involving remote sensing data. This paper presents a deep learning based CloudX-Net architecture, that can detect cloud cover with improved accuracy in comparison to the benchmark from satellite remote sensing images. The proposed CloudX-Net model reduces the number of parameters needed for accurate predictions and thus make deep learning based cloud detection method very efficient. Atrous Spatial Pyramid Pooling (ASPP) and Separable convolution are used to optimize the network. For experimentation, we have used Landsat 8 images and 38-Cloud dataset and trained the architectures using Soft Jaccard loss function. Comparing several quantifying metrics result from various recent deep learning architectures proves the efficiency and effectiveness of the proposed CloudX-Net model for cloud detection from satellite images. The source code and data are available at © 2020 Elsevier B.V.
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.