  The segmentation of satellite images is crucial in remote sensing
applications. Existing methods face challenges in recognizing small-scale
objects in satellite images for semantic segmentation primarily due to ignoring
the low-level characteristics of the underlying network and due to containing
distinct amounts of information by different feature maps. Thus, in this
research, a tri-level attention-based DeepLabv3+ architecture (DeepTriNet) is
proposed for the semantic segmentation of satellite images. The proposed hybrid
method combines squeeze-and-excitation networks (SENets) and tri-level
attention units (TAUs) with the vanilla DeepLabv3+ architecture, where the TAUs
are used to bridge the semantic feature gap among encoders output and the
SENets used to put more weight on relevant features. The proposed DeepTriNet
finds which features are the more relevant and more generalized way by its
self-supervision rather we annotate them. The study showed that the proposed
DeepTriNet performs better than many conventional techniques with an accuracy
of 98% and 77%, IoU 80% and 58%, precision 88% and 68%, and recall of 79% and
55% on the 4-class Land-Cover.ai dataset and the 15-class GID-2 dataset
respectively. The proposed method will greatly contribute to natural resource
management and change detection in rural and urban regions through efficient
and semantic satellite image segmentation
