Enhanced IT2FCM algorithm using object-based triangular fuzzy set modeling for remote-sensing clustering

Object-based fuzzy clustering method has been widely used to remote-sensing clustering analysis. Mean and interval spectral signatures are typically used to describe an object's features. However, accurately distinguishing two objects with the same mean or interval values and different internal distributions is difficult. Focus on this problem, we developed triangular fuzzy set modeling to describe objects and designed an interval distance metric to measure the dissimilarities between triangular fuzzy sets. Furthermore, using the variation of fuzzifier (two fuzzifiers) to construct interval type-2 fuzzy c-means (IT2FCM) clustering methods, which are sensitive to the choice of fuzzifier, has uncertainty and subjectivity. Thus, an enhanced IT2FCM clustering algorithm that directly adopts the interval distance metric rather than the variation of fuzzifier is proposed for high-resolution remote-sensing clustering. We performed land-cover classification experiments for three study areas by utilizing remote-sensing images from the SPOT-5 and Gaofen-2 satellite sensor, which spatial resolution are approximately 10 m and 1 m, respectively. Visual and numerical results, including Kappa coefficients and the confusion matrix, were utilized to verify the classification results. The experimental results indicated that triangular fuzzy set modeling is appropriate for extracting features from ground objects; moreover, it limits the classification errors caused by same objects with different spectral features. Compared with the object-based interval-valued fuzzy c-means (IV-FCM) method reported in the literature, the proposed algorithm results in improved classification quality and accuracy.