Deep Merge: Deep-Learning-Based Region Merging for Remote Sensing Image Segmentation

Abstract

Image segmentation represents a fundamental step in analyzing very high-spatial-resolution (VHR) remote sensing imagery. Its objective is to partition an image into segments that best match with geo-objects. However, the diverse appearances of geospatial objects often lead to interobject homogeneity and intraobject heterogeneity. Existing segmentation methods often struggle to accurately segment geo-objects with varying shapes and scales. To address these challenges, we propose DeepMerge, a novel method that integrates deep learning and region adjacency graphs (RAGs) to accurately segment complete geo-objects in large VHR images. DeepMerge begins with an initial over-segmentation of the image and then iteratively merges similar regions to achieve complete geo-object segmentation. A deep learning model is employed to learn the similarity between adjacent superpixel pairs. This approach only requires labels indicating whether adjacent superpixels belong to the same geo-object eliminating the need for object-level annotations, enabling weakly supervised segmentation. A cross-scale module is incorporated to capture multiscale information, enhancing the representation of superpixels. In addition, the feature distances between neighboring super-pixels are deemed as scale parameters (thresholds) to control the merging procedure, thus yielding an interpretable, predictable, stable, and optimal scale parameter 0.5. DeepMerge can achieve high segmentation accuracy in a weakly supervised manner, which is validated on large-scale remote sensing images of 0.55-m resolution covering an area of 5660 km2. The experimental results demonstrate that DeepMerge achieves the highest F value (0.9552) and the lowest total error (TE) (0.0827), accurately segmenting geo-objects of varying sizes and outperforming all competing methods.