An Empirical Study of the Convolution Neural Networks Based Detection on Object With Ambiguous Boundary in Remote Sensing Imagery—A Case of Potential Loess Landslide

Many objects in the naturalenvironment are generated from the background and even transformed by nature or human beings. Thus, they do not have closed and well-defined boundaries in remote sensing imagery. Recently, convolutional neural network (CNN) based object detection achieved great success in the remote sensing field. However, there is no investigation in the literature about the detection of objects with ambiguous boundaries. In this article, taking the case of the potential loess landslide detection, we designed massive experiments to evaluate convolutional neural networks for detecting objects with ambiguous boundaries in remote sensing imagery. We analyzed the evaluated methods comprehensively by comparing the performance on objects with ambiguous boundaries in remote sensing imagery with the performance on ordinary objects in visual imagery. Furthermore, drawing from these analyses, we provided a fundamental principle of object representation and a meaningful suggestion of information learning to detect objects with ambiguous boundaries. We finished this article by presenting several promising directions for detecting objects with ambiguous boundaries to facilitate and spur future research. This article would provide a significant reference and guidance to develop detectors for objects with ambiguous boundaries in remote sensing imagery.