Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey
Gulcan Sarp,Mehmet Ozcelik +1 more
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In this paper, the spectral and spatial performance of each classifier was compared using Pearson's r, the Structural Similarity Index Measure (SSIM), and the Root Mean Square Error (RMSE).About:
This article is published in Journal of Taibah University for Science.The article was published on 2017-05-01 and is currently open access. It has received 152 citations till now.read more
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Comparing thresholding with machine learning classifiers for mapping complex water
TL;DR: The study found that aggregating the thresholding results of two SAR and multispectral features, namely the S1 VH polarisation and the S2 NDWI, provided better overall accuracies than when thresholding was applied to any of the individual features considered, and may offer a viable solution for automatic mapping of waterbodies.
Journal ArticleDOI
Have coastal embankments reduced flooding in Bangladesh
TL;DR: It is concluded that whilst polders have provided protection against storm surges and fluvio-tidal events of moderate severity, they have exacerbated more frequent pluvial flooding and promoted potential flooding impacts during the most extreme storm surges.
Journal ArticleDOI
Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification
Vanessa Sousa da Silva,Gabriela Salami,Marília Isabelle Oliveira da Silva,Emanuel Araújo Silva,José Jorge Monteiro Junior,Elisiane Alba +5 more
TL;DR: In this paper, the authors evaluated the spectral properties of vegetation spectral behavior in relation to the soil and other terrestrial surface targets, and the objective of this study was to evaluate the vegetati...
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Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area
TL;DR: In this article, the future form of Land Surface Temperature (LST) distribution and Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area was simulated using spectral indices, namely NDBI and NDVI.
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Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data
TL;DR: An unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination to be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.