  Image segmentation aims to partition an image according to the objects in the
scene and is a fundamental step in analysing very high spatial-resolution (VHR)
remote sensing imagery. Current methods struggle to effectively consider land
objects with diverse shapes and sizes. Additionally, the determination of
segmentation scale parameters frequently adheres to a static and empirical
doctrine, posing limitations on the segmentation of large-scale remote sensing
images and yielding algorithms with limited interpretability. To address the
above challenges, we propose a deep-learning-based region merging method dubbed
DeepMerge to handle the segmentation of complete objects in large VHR images by
integrating deep learning and region adjacency graph (RAG). This is the first
method to use deep learning to learn the similarity and merge similar adjacent
super-pixels in RAG. We propose a modified binary tree sampling method to
generate shift-scale data, serving as inputs for transformer-based deep
learning networks, a shift-scale attention with 3-Dimension relative position
embedding to learn features across scales, and an embedding to fuse learned
features with hand-crafted features. DeepMerge can achieve high segmentation
accuracy in a supervised manner from large-scale remotely sensed images and
provides an interpretable optimal scale parameter, which is validated using a
remote sensing image of 0.55 m resolution covering an area of 5,660 km^2. The
experimental results show that DeepMerge achieves the highest F value (0.9550)
and the lowest total error TE (0.0895), correctly segmenting objects of
different sizes and outperforming all competing segmentation methods.
