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Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System: a hybrid method for feature selection and land pattern classification

Bui, Quang-Thanh, Pham, Manh Van, Nguyen, Quoc-Huy, Nguyen, Linh Xuan, Pham, Hai Minh
International journal of remote sensing 2019 v.40 no.13 pp. 5078-5093
algorithms, case studies, computer software, data collection, fuzzy logic, land cover, model validation, remote sensing, Vietnam
Adaptive Neuro-Fuzzy Inference System (ANFIS) is a robust method in solving non-linear classification by employing a human-readable interpretation manner. This paper verified a hybrid model, named WANFIS, where Whale Optimization Algorithm (WOA) was used for feature selection and tuning parameters of the ANFIS for land-cover classification. Hanoi, the capital of Vietnam, was selected as a case study, because of its complex surface morphology. The model was trained and validated with different data sets, which were subsets of the segmented objects from SPOT 7 satellite data (1.5 m in panchromatic and 6 m multiple spectral bands). The image segmentation was carried out by using PCI Geomatics software (evaluation version), and output objects with associated spectral, shape, and metric information were selected as input data to train and validate the proposed model. For accuracy assessment, the performance of the model was compared to several benchmarked classifiers by using standard statistical indicators such as Receiver Operator Characteristics, Area under ROC, Root Mean Square Error, Absolute Mean Error, Kappa index, and Overall accuracy. The results showed that WANFIS outperformed the other in almost all training data sets for both operations. It could be concluded that the examination of the classification model in different training data sizes is significant, and the proper determination of predictor variables and training sizes would improve the quality of classification of remotely sensed data.