Classification of very high-resolution remote sensing images by applying a new edge-based marker-controlled watershed segmentation method

Abstract

Recent advances in remote sensing technology have led to the creation of many sensors with very high spatial resolution, which enable researchers to detect and recognize earth surface features precisely in fine detail. There is an increasing demand for the development of algorithms to extract geometrical and spatial information from these kinds of images as the prerequisite for accurate thematic map generation. Among the existing techniques, segmentation-based methods are of great interest due to their capabilities in extracting spatial information of the image. In this article, a new watershed segmentation approach is proposed where the image edge information is exploited to improve the results. Next to its simplicity, the main characteristic of this newly proposed method is that there is no need to set any effective parameter(s), a common drawback confronted by most state-of-the-art segmentation techniques. The method is first applied to a well-known dataset, and the result of the segmentation phase is evaluated. The segmentation and classification phases of our approach are then both applied to selected Pleiades and WorldView-2 images. The final spatial–spectral classification map is compared to the results obtained through three common segmentation-based classification methods. The result of this comparison indicates that our approach outperforms the others by having 94.94% overall accuracy for a Pleiades image and 89.81% overall accuracy for a WorldView-2 image.