Support Vector Machine algorithm optimal parameterization for change detection mapping in Funil Hydroelectric Reservoir (Rio de Janeiro State, Brazil) - Martins, Sarah [UNESP]; Bernardo, Nariane [UNESP]; Ogashawara, Igor; Alcantara, Enner [UNESP] (Springer, 2016)
Change detection in Land Use and Land Cover (LULC) using Support Vector Machines (SVM) to mapping a geographic area is a way that has been studded and improved because of its advantages as low costs in terms of computing, field research and staff team. To use SVM, it is needed firstly to define the most efficient function to be used (linear, polynomial, and radial base function-RBF) and secondly to establish the most appropriate input parameters of them, based on the study area, which was the main challenge of using SVM algorithm. The main goal of this work was to test the performance of polynomial function and RBF, and to identify which input parameters combination are the best to use SVM algorithm for Funil Hydroelectric Reservoir (FHR) sub-watershed LULC mapping, using TM/Landsat-5 time-series images. After several tests and based on the obtained results, the RBF was identified as the best SVM's function, which was used to classify the time-series images. The referred SVM function has the following parameters to be defined: the error tolerance (n or C), the pyramid depths (P), the radial basis function parameter (gamma), and the threshold. The most effective combination of input parameters to RBF was C = 100; P = 2, gamma = 0.1, threshold = 0.05. LULC change detection analyses demonstrates that the obtained SVM parameterization made the algorithm able to differentiate large and continuous classes, lengthy and thin areas, as borders, and not continuous small areas located inside wide classes, through the usage of effective, but small, training sample. The parameterization proposed for this work to FHR sub-watershed area resulted in great statistics classification with the overall's accuracy among 86 and 98 % over the time-series, the producer's accuracy of 90 %, the user's accuracy higher than 86 %, and the Kappa statistics ranged from 86 to 91 %.
Keywords: SVM parameters; Change detection; TM/Landsat-5; Automatic classification