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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Controllably Deep Supervision and Multi-Scale Feature Fusion Network for Cloud and Snow Detection Based on Medium- and High-Resolution Imagery Dataset
TL;DR: Wang et al. as mentioned in this paper proposed a convolutional neural network for cloud and snow detection, named the Cloud and Snow detection network (CSD-Net), which incorporates the multi-scale feature fusion module (MFF) and the controllably deep supervision and feature fusion structure (CDSFF).
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Integrating a Three-Level GIS Framework and a Graph Model to Track, Represent, and Analyze the Dynamic Activities of Tidal Flats
TL;DR: In this paper, an integrated approach of a three-level Geographic Information Science (GIS) framework and a graph model is proposed to better track, represent, and analyze the dynamic activities of the tidal flats.
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ClouDet: A Dilated Separable CNN-Based Cloud Detection Framework for Remote Sensing Imagery
TL;DR: Li et al. as discussed by the authors proposed a lightweight deep-learning-based framework to detect clouds in remote sensing imagery, where a multiple features fusion strategy is designed to extract learnable manual features and convolution features from visible and near-infrared bands.
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A deep learning model for incorporating temporal information in haze removal
TL;DR: In this paper , a temporal information injection network (TIIN) is proposed to extract the useful information in the temporally neighboring images provided by the regular revisit of satellite sensors, which is suitable for images with various haze levels.
Journal ArticleDOI
Operational large-area land-cover mapping: An Ethiopia case study
Reza Khatami,Jane Southworth,Carly Muir,T. Trevor Caughlin,Alemayehu N. Ayana,Daniel G. Brown,Chuan Liao,Arun Agrawal +7 more
TL;DR: Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land- cover products in a timely fashion.
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.
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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.