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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Glacial Lake Area Changes in High Mountain Asia during 1990–2020 Using Satellite Remote Sensing
TL;DR: Wang et al. as discussed by the authors developed a per-pixel composited method named the "multitemporal mean NDWI composite" to automatically extract the glacial lake area in HMA from 1990 to 2020 using time-series Landsat data.
Journal ArticleDOI
Convolutional Neural Networks for On-Board Cloud Screening
Sina Ghassemi,Enrico Magli +1 more
TL;DR: This study investigates experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy and shows that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art.
Journal ArticleDOI
Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack
TL;DR: Wang et al. as mentioned in this paper presented a time-series land-cover mapping approach by combining the Continuous Change Detection and Classification (CCDC) algorithm with Markov Random Field (MRF) model to explore the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat time series.
Journal ArticleDOI
Forestation does not necessarily reduce soil erosion in a karst watershed in southwestern China
TL;DR: In this article, the authors investigated whether forestation has effectively reduced soil erosion in southwestern China by using returning farmland to forest project (RFPT) to mitigate soil erosion.
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A practical and automated approach to large area forest disturbance mapping with remote sensing.
TL;DR: A set of procedures that automate forest disturbance mapping using a pair of Landsat images built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data.
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.