Integrating spectral and textural attributes to measure magnitude in object-based change vector analysis

Abstract

ABSTRACTWith the increasing accessibility of high or very high spatial resolution remote sensing images, more and more change detection works are conducted on the unit of image object, i.e. the object-based change detection (OBCD). Change vector analysis (CVA) is a promising tool for unsupervised OBCD because it can provide reasonable interpretation of the change and an insight into the type of change. However, various features and attributes of image object produce complex high dimensional feature space that poses new challenges to measure the overall change magnitude and direction in CVA. This paper presents a new approach to measure the overall change magnitude of an image object by integrating its spectral and textural attributes, self-adaptively. The new approach was compared with the standard CVA magnitude, Mahalanobis distance, and a recent Self-Adaptive Weight CVA (SAW-CVA) magnitude for unsupervised OBCD with the same OTSU auto-thresholding method. Two cases were investigated where Case I used bi-temporal WorldView multispectral images (4-band) and Case II employed bi-temporal three-band images obtained from Google Earth. Results in the two cases both demonstrated that the new approach outperforms the others for unsupervised OBCD. The percentage correct classification of the new approach, SAW-CVA, standard CVA, and Mahalanobis are 80.28%, 77.93%, 72.77%, and 61.50% in Case I, and 91.80%, 90.16%, 88.52%, and 90.16% in Case II. Further analysis indicated that the new approach gives more reasonable self-adaptive weights and it does not require the empirical self-adaptive index as compared with the recent SAW-CVA method.