An adaptively weighted multi-feature method for object-based change detection in high spatial resolution remote sensing images

ABSTRACT

In this letter, an adaptively weighted multi-feature-based method for unsupervised object-based change detection in high-resolution remote sensing images is proposed. First, a sample selection strategy using fuzzy c-means is designed to obtain high precision pseudo-samples in an unsupervised way. Second, the multiple candidate features are categorized into spectral, geometric and textural groups and two kinds of weights are involved taking into account different contributions. The within-group weights for each feature can be calculated based on single-feature distribution curve without any prior distribution assumption, and the between-group weights for each group are decided by scatter matrices. Third, the weighted multi-feature method is used to generate a reliable difference image which is directly clustered to obtain the final change map. Compared with the other five state-of-the-art methods, the experimental results on two datasets demonstrate the effectiveness and superiority of the proposed method.

Additional information

Funding

This work was supported by the State Key Laboratory Fund [SKLLIM1808].