Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications
Z Zhu - ISPRS Journal of Photogrammetry and Remote …, 2017 - Elsevier
The free and open access to all archived Landsat images in 2008 has completely changed
the way of using Landsat data. Many novel change detection algorithms based on Landsat …
the way of using Landsat data. Many novel change detection algorithms based on Landsat …
Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects
The presence of clouds prevents optical satellite imaging systems from obtaining useful
Earth observation information and negatively affects the processing and application of …
Earth observation information and negatively affects the processing and application of …
Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery
We developed the Function of mask (Fmask) 4.0 algorithm for automated cloud and cloud
shadow detection in Landsats 4–8 and Sentinel-2 images. Three major innovative …
shadow detection in Landsats 4–8 and Sentinel-2 images. Three major innovative …
Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning
Cloud cover is a common and inevitable phenomenon that often hinders the usability of
optical remote sensing (RS) image data and further interferes with continuous cartography …
optical remote sensing (RS) image data and further interferes with continuous cartography …
Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
Cloud detection is an important preprocessing step for the precise application of optical
satellite imagery. In this paper, we propose a deep learning based cloud detection method …
satellite imagery. In this paper, we propose a deep learning based cloud detection method …
Cloud detection in remote sensing images based on multiscale features-convolutional neural network
Cloud detection in remote sensing images is a challenging but significant task. Due to the
variety and complexity of underlying surfaces, most of the current cloud detection methods …
variety and complexity of underlying surfaces, most of the current cloud detection methods …
Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks
This paper formulates cloud and cloud shadow detection as a semantic segmentation
problem and proposes a deep convolutional neural network (CNN) based method to detect …
problem and proposes a deep convolutional neural network (CNN) based method to detect …
Cloud-Net: An end-to-end cloud detection algorithm for Landsat 8 imagery
S Mohajerani, P Saeedi - IGARSS 2019-2019 IEEE …, 2019 - ieeexplore.ieee.org
Cloud detection in satellite images is an important first-step in many remote sensing
applications. This problem is more challenging when only a limited number of spectral …
applications. This problem is more challenging when only a limited number of spectral …
Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning
Thick cloud and its shadow severely reduce the data usability of optical satellite remote
sensing data. Although many approaches have been presented for cloud and cloud shadow …
sensing data. Although many approaches have been presented for cloud and cloud shadow …
CDnet: CNN-based cloud detection for remote sensing imagery
Cloud detection is one of the important tasks for remote sensing image (RSI) preprocessing.
In this paper, we utilize the thumbnail (ie, preview image) of RSI, which contains the …
In this paper, we utilize the thumbnail (ie, preview image) of RSI, which contains the …