Journal ArticleDOI
Object-based cloud and cloud shadow detection in Landsat imagery
Zhe Zhu,Curtis E. Woodcock +1 more
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The goal is development of a cloud and cloud shadow detection algorithm suitable for routine usage with Landsat images and as high as 96.4%.About:
This article is published in Remote Sensing of Environment.The article was published on 2012-03-15. It has received 1620 citations till now. The article focuses on the topics: Cloud top & Cloud fraction.read more
Citations
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Comparison of cloud detection algorithms for Sentinel-2 imagery
Katelyn Tarrio,Xiaojing Tang,Jeffrey G. Masek,Martin Claverie,Junchang Ju,Shi Qiu,Zhe Zhu,Curtis E. Woodcock +7 more
TL;DR: Aggregating the results from multiple algorithms produces fewer undetected clouds and higher overall accuracy than any single algorithm, suggesting an ensemble approach may be the most useful for processing of Sentinel-2 data.
Journal ArticleDOI
Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data
TL;DR: This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and Recovery each can be detected in a subtropical region of eastern Zhejiang Province, China through the LandTrendr algorithm.
Journal ArticleDOI
Smart Information Reconstruction via Time-Space-Spectrum Continuum for Cloud Removal in Satellite Images
TL;DR: Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels.
Journal ArticleDOI
Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2
TL;DR: The first Cloud Masking Intercomparison Exercise (CMIX) as discussed by the authors was conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV).
Journal ArticleDOI
Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images
TL;DR: A machine learning algorithm named Multi-Layer Perceptron (MLP) was used in this paper to establish a novel automatic method to monitor MAB continuously in the Yellow Sea, using Geostationary Ocean Color Imager (GOCI) imagery.
References
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Book
Morphological Image Analysis: Principles and Applications
TL;DR: This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.
Journal ArticleDOI
A Landsat surface reflectance dataset for North America, 1990-2000
Jeffrey G. Masek,Eric Vermote,Nazmi Saleous,Robert E. Wolfe,Forrest G. Hall,Karl F. Huemmrich,Feng Gao,J. Kutler,Teng-Kui Lim +8 more
TL;DR: Initial comparisons with ground-based optical thickness measurements and simultaneously acquired MODIS imagery indicate comparable uncertainty in Landsat surface reflectance compared to the standard MODIS reflectance product.
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Discriminating clear sky from clouds with MODIS
Steven A. Ackerman,Kathleen I. Strabala,W. Paul Menzel,Richard A. Frey,Christopher C. Moeller,Liam E. Gumley +5 more
TL;DR: The MODIS cloud mask algorithm as discussed by the authors uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies, which is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options.
Journal ArticleDOI
Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data
TL;DR: Zhang et al. as discussed by the authors used a more advanced NASA Goddard Institute for Space Studies (GISS) radiative transfer model and improved ISCCP cloud climatology and ancillary data sets.
Journal ArticleDOI
Spectral signature of alpine snow cover from the Landsat Thematic Mapper.
Jeff Dozier,Jeff Dozier +1 more
TL;DR: In this article, the spectral signatures of the Landsat TM images of the Sierra Nevada were analyzed to distinguish several classes of snow from other surface covers, and a number of TM images were used for automatic analysis of alpine snow cover.