remote sensing imagery
Monitoring agricultural plantations with remote sensing imagery
... Fig 8. Histogram RGB 123 Also for comparison the Mostistea surface we used and satellite remote sensing imagery provided by SPOT in 2007 (Fig.9). The current plans for SPOT – 5 envision the ...
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Anomaly Detection from Hyperspectral Remote Sensing Imagery
... Abstract: Hyperspectral remote sensing imagery contains much more information in the spectral domain than does multispectral imagery. The consecutive and abundant spectral signals provide a ...
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Remote Sensing / An object-based semantic classification method for high resolution remote sensing imagery using ontology
... HRS remote sensing images at the regional level ...resolution remote sensing imagery using ontology that enables a common understanding of the GEOBIA framework structure for human ...
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Noise estimation in remote sensing imagery using data masking
... Abstract. Estimation of noise contained within a remote sensing image is essen- tial in order to counter the effects of noise contamination. The application of convolution data-masking techniques can ...
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Uncertainty Assessment of Spectral Mixture Analysis in Remote Sensing Imagery
... APPROACH FOR ANALYZING URBAN ENVIRONMENTS 3 4.1 Introduction Spectral mixture analysis (SMA) has been widely applied to address the mixed pixel problem, a typical issue associated with medium- and coarse-resolution ...
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Allometric equations for integrating remote sensing imagery into forest monitoring programmes
... integrate remote sensing imagery – particu- larly ALS data – into forest monitoring programmes, allowing carbon stocks to be mapped with accuracy across forest landscapes and shedding light on the ...
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Allometric equations for integrating remote sensing imagery into forest monitoring programmes
... integrate remote sensing imagery – particu- larly ALS data – into forest monitoring programmes, allowing carbon stocks to be mapped with accuracy across forest landscapes and shedding light on the ...
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A Secure and Efficient Method for Remote Sensing Imagery Distribution in Cloud Computing
... for remote sensing image (RSI), current most marking operations would cause a little information loss on the marked ...for remote sensing imagery is ...
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Road Recognition from Remote Sensing Imagery using Machine Learning
... In Remote sensing systems one of the most important features needed are roads, which require feature extraction to identify them from high-resolution satellite ...from remote sensing ...
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Automatic Cloud Detection and Removal Algorithm for MODIS Remote Sensing Imagery
... Abstract— Cloud is one of the most common interferers in Moderate Resolution Imaging Spectrum-radiometer (MODIS) remote sensing imagery. Because of cloud interference, much important and useful ...
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Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery
... of remote sensing ...from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow ...
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Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California
... using remote sensing imagery to assist in the mapping and monitoring of wildfires in San Diego County, and I would particularly recommend using satellite imagery such as IKONOS, which provides ...
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Advanced Methods for the Retrieval of Geo-/Bio-Physical Variables from Remote Sensing Imagery
... satellite remote sensing ...to remote sensing data to assess their ...the remote sensing technology for mapping and monitoring natural resources and physical processes on the ...
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Regularization destriping of remote sensing imagery
... Abstract. We illustrate the utility of variational destriping for ocean color images from both multispectral and hyper- spectral sensors. In particular, we examine data from a filter spectrometer, the Visible Infrared ...
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Feature extraction and fusion for classification of remote sensing imagery
... the remote sensing field have contributed significantly at the broad availability of high quality data or ...different remote sensing data sets contain, is a necessary pre-processed ...
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Multi-Level Manifolds for Remote Sensing Imagery Classification
... the remote sensing data doesn’t guarantee the accurate feature extraction but it offers the objectives of the user in an extensive ...in remote sensing data is a significant factor because of ...
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Landslide detection using satellite remote sensing imagery
... 2 Department of Natural Resources, Chinese Culture University, 55, Hwa Kang Rd, Yangmingshan, Taipei, Taiwan 3 Department of Geography, Chinese Culture University, 55, Hwa Kang Rd, Yangmingshan, Taipei, Taiwan ARTICLE ...
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ISBDD model for classification of hyperspectral remote sensing imagery
... Figure 10 illustrates the impact of the intensity of interfered pixels in training samples on classification accuracy when the four classifiers are used for the Indian Pines image.. The[r] ...
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Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
... The domain adaptation techniques used in this paper could be greatly improved as the quantity and quality of DIRSIG scenes improves as well. In the future, we could pull data from multiple DIRSIG scenes, increase the ...
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LEARNING CLASSIFIERS FOR SCIENCE EVENT DETECTION IN REMOTE SENSING IMAGERY
... EO-1 is part of NASA’s New Millennium Program, designed to validate new technologies for remote sensing. It was launched from Vandenberg Air Force Base on 21 November 2000 and placed in a sun- synchronous ...
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