Feature Guide Network With Context Aggregation Pyramid for Remote Sensing Image Segmentation

In recent years, the deep learning method based on fully convolution networks has proven to be an effective method for the semantic segmentation of remote sensing images (RSIs). However, the rich information and complex content of RSIs make networks training for segmentation more challenging. Specifically, the observing distance between the space-borne cameras and the ground objects is extraordinarily far, resulting in that some smaller objects only occupy a few pixels in the image. However, due to the rapid degeneration of tiny objects during the training process, most algorithms cannot properly handle these common small objects in RSIs with satisfactory results. In this article, we propose a novel feature guide network with a context aggregation pyramid (CAP) for RSIs segmentation to conquer these issues. An innovative edge-guide feature transform module is designed to take advantage of the edge and body information of objects to strengthen edge contours and the internal consistency in homogeneous regions, which can explicitly enhance the representation of tiny objects and relieve the degradation of small objects. Furthermore, we design a CAP pooling strategy to adaptively capture optimal feature characterization that can assemble multiscale features according to the significance of different contexts. Extensive experiments on three large-scale remote sensing datasets demonstrate that our method not only can outperform the state-of-the-art methods for objects of different scales but can also achieve robust segmentation results, especially for tiny objects.