Assessment of different classification algorithms for burnt land discrimination

Zammit, Olivier; Descombes, Xavier; Zerubia, Josiane

doi:10.1109/igarss.2007.4423476

Cited by 3 publications

(1 citation statement)

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“…Up until now, several classification techniques have been applied in burned area mapping, including maximum likelihood classification [13,14], logistic regression [15], classification and regression trees [14,16] linear and/or nonlinear spectral mixture analysis [17,18], thresholding of Vegetation Indices (VIs) [14,19], Neural Networks [20], Neuro-Fuzzy techniques [21], Support Vector Machines (SVMs) [22][23][24], and Object Based Image Analysis (OBIA) [25,26]. However, the selection of the optimal method each time depends on several factors, such as the scale and the goals of the current project.…”

Section: Introductionmentioning

confidence: 99%

Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery

et al. 2014

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Abstract:The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image analysis techniques: (a) an efficient feature selection method based on the Fuzzy Complementary Criterion (FuzCoC) and (b) the Support Vector Machine (SVM) classifier. In both cases (pixel and object-based), a number of higher-order spectral and spatial features were produced from the original image. The proposed methodology was tested in areas of Greece recently affected by severe forest fires, namely, Parnitha and Rhodes. The extensive comparative analysis indicates that the SVM object-based scheme exhibits higher classification accuracy than the respective pixel-based one. Additionally, the accuracy increased with the addition of derivative features and subsequent implementation of the FuzCoC feature selection (FS) method. Apart from the positive effect in the classification accuracy, the application of the FuzCoC FS method significantly reduces the computational requirements and facilitates the manipulation of the large data volume. In both cases (pixel and objet) the results confirmed that the use of an efficient feature selection method is a prerequisite step when extra information through OPEN ACCESS Remote Sens. 2014, 6 12006 higher-order features is added to the classification process of VHR imagery for burned area mapping.

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