  Semantic segmentation of remote sensing imagery plays a pivotal role in
extracting precise information for diverse down-stream applications. Recent
development of the Segment Anything Model (SAM), an advanced general-purpose
segmentation model, has revolutionized this field, presenting new avenues for
accurate and efficient segmentation. However, SAM is limited to generating
segmentation results without class information. Consequently, the utilization
of such a powerful general vision model for semantic segmentation in remote
sensing images has become a focal point of research. In this paper, we present
a streamlined framework aimed at leveraging the raw output of SAM by exploiting
two novel concepts called SAM-Generated Object (SGO) and SAM-Generated Boundary
(SGB). More specifically, we propose a novel object loss and further introduce
a boundary loss as augmentative components to aid in model optimization in a
general semantic segmentation framework. Taking into account the content
characteristics of SGO, we introduce the concept of object consistency to
leverage segmented regions lacking semantic information. By imposing
constraints on the consistency of predicted values within objects, the object
loss aims to enhance semantic segmentation performance. Furthermore, the
boundary loss capitalizes on the distinctive features of SGB by directing the
model's attention to the boundary information of the object. Experimental
results on two well-known datasets, namely ISPRS Vaihingen and LoveDA Urban,
demonstrate the effectiveness of our proposed method. The source code for this
work will be accessible at https://github.com/sstary/SSRS.

Computer Vision and Pattern Recognition