Remote Sensing Classification of Hurricane Storm Surge Structural Damage

2007 ◽  
Author(s):  
Carol J. Friedland ◽  
Beverley J. Adams ◽  
Marc L. Levitan
Keyword(s):  
Remote Sensing ◽  
Storm Surge ◽  
Structural Damage ◽  
Remote Sensing Classification ◽  
Hurricane Storm Surge

Remote Sensing Classification of Marsh Wetland with Different Resolution Images

2016 ◽  
Vol 7 (2) ◽  
pp. 107-114 ◽  
Author(s):  
Li Na ◽  
Xie Gaodi ◽  
Zhou Demin ◽  
Zhang Changshun ◽  
Jiao Cuicui
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Classification ◽  
Marsh Wetland

Remote sensing classification of grass seed cropping practices in western Oregon

2011 ◽  
Vol 32 (9) ◽  
pp. 2451-2480 ◽  
Author(s):  
George W. Mueller-Warrant ◽  
Gerald W. Whittaker ◽  
Stephen M. Griffith ◽  
Gary M. Banowetz ◽  
Bruce D. Dugger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Classification ◽  
Grass Seed ◽  
Cropping Practices

scholarly journals A new approach to the application of conflict redistribution rule in Satellite Image Classification

2020 ◽  
pp. 12-16
Author(s):  
Sofiia Alpert
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Classification Methods ◽  
New Approach ◽  
Remote Sensing Classification ◽  
Satellite Image Classification ◽  
Bodies Of Evidence

Nowadays solution of different scientific problems using satellite images, generally includes a classification procedure. Classification is one of the most important procedures used in remote sensing, because it involves a lot of mathematical operations and data preprocessing. The processing of information and combining of conflicting data is a very difficult problem in classification tasks. Nowadays many classification methods are applied in remote sensing. Classification of conflicting data has been a key problem, both from a theoretical and practical point of view. But a lot of known classification methods can not deal with highly conflicted data and uncertainty. The main purpose of this article is to apply proportional conflict redistribution rule (PRC5) for satellite image classification in conditions of uncertainty, when conflicting sources of evidence give incomplete and vague information. This rule can process conflicting data and combine conflicting bodies of evidence (spectral bands). Proportional conflict redistribution rule can redistribute the partial conflicting mass proportionally on non-empty sets involved in the conflict. It was noticed, that this rule can provide a construction of aggregated estimate under conflict. It calculates all partial conflicting masses separately. It was also shown, that proportional conflict redistribution rule is the most mathematically exact redistribution of conflicting mass to non-empty set. But this rule consists of difficult calculation procedures. The more hypotheses and more masses are involved in the fusion, the more difficult is to implement proportional conflict redistribution rule, therefore special computer software should be used. It was considered an example of practical use of the proposed conflict redistribution rule. It also was noticed, that this new approach to the application of conflict redistribution rule in satellite image classification can be applied for analysis of satellite images, solving practical and ecological tasks, assessment of agricultural lands, classification of forests, in searching for oil and gas.

scholarly journals Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

2021 ◽  
Vol 13 (24) ◽  
pp. 5064
Author(s):  
Yanpeng Yang ◽  
Dong Yang ◽  
Xufeng Wang ◽  
Zhao Zhang ◽  
Zain Nawaz
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Qilian Mountains ◽  
Classification Algorithms ◽  
Cloud Platform ◽  
Remote Sensing Classification ◽  
The Qilian Mountains ◽  
Sentinel 2

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

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