Multi-scale hierarchical sampling change detection using Random Forest for high-resolution satellite imagery

High-resolution imagery provides rich information useful for land-use and land-cover change detection; however, methods to exploit these data lag behind data collection technologies. In this article, we propose a novel object-oriented multi-scale hierarchical sampling (MSHS) change detection method for high-resolution satellite imagery. In our method, MSHS is carried out to automatically obtain multi-scale training samples and different sample combinations. The training sample spectra, texture, and shape features are fused to build feature space after MSHS. Sample combinations and corresponding feature spaces are input into Random Forest (RF) to train multiple change classifiers. An optimal RF change detection classifier is selected when the out-of-bag error parameter in RF is at the minimum. In order to validate the proposed method, we applied it to high-resolution satellite image data and compared the detection results from our method and the single-scale sampling change detection method. These experimental results show that false alarm rates and missed detection of changed objects using our method were lower than the single-scale sampling change detection method. To demonstrate the scalability of the algorithm, different change detection methods were applied to three study sites. Experimental results show that our method delivered high overall accuracy and F-1-scores. Compared to traditional methods, our method makes full use of the multi-scale characteristics of ground objects. Our approach does not extend multi-scale feature vectors directly, but instead automatically increases the amount of the training samples at multiple scales, without increasing the volume of manual processing, thus improving the ability of the algorithm to generalize features from the RF model, making it more robust.