Machine learning-assisted region merging for remote sensing image segmentation

With the increasing popularity of OBIA, many scholars advocate that image segmentation plays a significant role in remote sensing image processing. Numerous segmentation algorithms for remote sensing images are based on region merging. Although good improvement is achieved, their accuracy is still dependent on parameter settings, leading to a low level of automation. To overcome this issue, this work proposes a new region merging method by using a random forest (RF) classifier. Unlike the traditional region merging methods that all adopt a scale threshold to determine whether a merging can be conducted, the new algorithm relies on a trained RF to decide the result of a merging test. Various merging criteria are simultaneously employed as feature variables of the RF model, enhancing the quality of the proposed scheme. The major problem in this work is how to train the RF classifier since the merging test samples need to be obtained in the iterative steps of a region merging process, which involves a huge number of human-computer interactions even for a small image. To simplify it, a sample collection strategy based on a set of three-scale segmentation results is devised. Representative merging test samples can be obtained by using this method. To validate the proposed technique, four Gaofen-2 images are used for training sample collection, and the most interesting result is that the samples extracted from one image can apply to others. Some images captured by Orbview-3, GeoEye-1, and Worldview-2 further indicate the robust performance of the new algorithm and the samples acquired in this work.