Abstract:
Semantic segmentation is an important job in computer vision, and its applications
have grown in popularity over the last decade. When we talk about remote sense
imaging, different aspects are there which proved to be impacted factors while processing
RS Data. Many survey applications, such as traffic monitoring, forest identification,
and other natural calamities, employ these imagery. A large variety of models have
been presented, the majority of them are based on CNN models and have obtained
the best results so far. In recent years, models based on Transformers have gained
popularity. With a few tweaks, we employ a transformer-based swin model. Because
the fundamental disadvantage of transformer-based models is that they demand a lot
of memory and processing resources. We offer the swin-model with a decreased number
of blocks and MLP-Head, which is utilized to speed up the model and to address these
concerns. In the case of RS-Data, edge improvement necessitates extra care. "Explicit Edge-Enhancement" and "Implicit-Edge-Enhancement" are two sophisticated strategies
we deploy. The datasets Vaihingen and Potsdam are used in the TrSeg-RS model. The
model improved its accuracy and found the best between Flops and mIoU. The vivid
improvements in findings show that the TrSeg-RS can play a key role in RS issues
