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Journal ArticleDOI

Object-based cloud and cloud shadow detection in Landsat imagery

Zhe Zhu, +1 more
- 15 Mar 2012 - 
- Vol. 118, pp 83-94
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TLDR
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.

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Citations
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Journal ArticleDOI

Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery

TL;DR: Deep learning models able to efficiently and accurately classify cloud, shadow and land cover scenes in different high-resolution satellite imagery scenes are developed.
Journal ArticleDOI

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

TL;DR: This study proposes a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images, and indicates that this method is effective for the large-scale mapping of winter crops.
Journal ArticleDOI

Land claim and loss of tidal flats in the Yangtze Estuary

TL;DR: An automatic algorithm is developed that estimates the area of tidal flat areas in the Yangtze Estuary based on the Land Surface Water Index and the Normalized Difference Vegetation Index to highlight the need for better coastal planning and management based on tidal flat dynamics.
Journal ArticleDOI

Mapping Brazilian savanna vegetation gradients with Landsat time series

TL;DR: Dense Landsat time series are explored to derive phenological information for mapping vegetation gradients in the Cerrado and show that it is possible to map the main spatial patterns of the observed physiognomies based on their phenological differences, emphasizing the need for remote sensing based time series analyses at fine scales.
Journal ArticleDOI

Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region, USA

TL;DR: In this article, the authors compared the timing, extent, and cumulative impacts of recent mountain pine beetle and western spruce budworm outbreaks across forested ecoregions of the US Pacific Northwest Region (PNW; Oregon and Washington).
References
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Book

Morphological Image Analysis: Principles and Applications

Pierre Soille
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

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.
Journal ArticleDOI

Discriminating clear sky from clouds with MODIS

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.

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.
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