[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: Volume 10, Issue 3 (2-2023) ::
jgit 2023, 10(3): 49-70 Back to browse issues page
An efficient method using the fusion of deep convolutional neural network features for cloud detection using Landsat-8 OLI spectral bands
Arastou Zarei, Reza Shah-Hosseini *, Morteza Seyyed-Mousavi
University of Tehran
Abstract:   (471 Views)
Cloud segmentation is a critical pre-processing step for any multi-spectral satellite image application. In particular, disaster-related applications e.g., flood monitoring or rapid damage mapping, which are highly time and data-critical, require methods that produce accurate cloud masks in a short time while being able to adapt to large variations in the target domain (induced by atmospheric conditions, different sensors, and scene properties). This research presented a deep convolutional neural network for cloud detection in the Landsat-8 dataset at the pixel level. Two key components of the proposed network are convolutional layers in the decoder branch and two convolution kernels in various scales. The near-infrared band in this study was added to the network inputs, including red, green, and blue bands, in order to improve the network performance. In the proposed network architecture, the encoder-decoder branches which are symmetrical with the density of feature maps resulting from the multiplicity of filters and the designing of multi-dimension filters, provided a local and general context for the accurate identification of the cloud and its margins which are used to extract the spatial features in high-level scales. However, multi-scale feature maps will be sampled and integrated to accuracy o-generate high Finally, the proposed method uses 3500 patches of Landsat-8 satellite images with various cloud challenges by using several kernels in sizes 3 x 3 and 5 x 5 with an F1-score of 96.6 and a Jaccard index (JI) of 93.5, provides a higher accuracy than the other methods. In general, the suggested method outperformed the alternatives in the same, uncorrected data set in terms of accuracy, particularly in regions with bright surfaces. Due to the effectiveness of the proposed framework, it has a lot of potential for practical application with different types of satellite images.
Keywords: Remote Sensing, Landsat-8, Convolution Neural Network, Cloud Detection
Full-Text [PDF 1461 kb]   (135 Downloads)    
Type of Study: Research | Subject: RS
Received: 2021/07/21 | Accepted: 2022/11/22 | Published: 2023/02/6
Send email to the article author

XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Zarei A, Shah-Hosseini R, Seyyed-Mousavi M. An efficient method using the fusion of deep convolutional neural network features for cloud detection using Landsat-8 OLI spectral bands. jgit 2023; 10 (3) :49-70
URL: http://jgit.kntu.ac.ir/article-1-839-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 10, Issue 3 (2-2023) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
Persian site map - English site map - Created in 0.04 seconds with 29 queries by YEKTAWEB 4570