Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers
This work addresses the challenge of efficiently segmenting large, high-resolution remote sensing images, which is incremental as it builds on existing CNNs and transformers with specific enhancements.
The paper tackles semantic segmentation of high-resolution remote sensing images by proposing a model that combines CNNs and transformers with multi-modal fusion layers and a multi-task strategy, achieving improved segmentation accuracy compared to state-of-the-art methods.
Semantic segmentation necessitates approaches that learn high-level characteristics while dealing with enormous amounts of data. Convolutional neural networks (CNNs) can learn unique and adaptive features to achieve this aim. However, due to the large size and high spatial resolution of remote sensing images, these networks cannot analyze an entire scene efficiently. Recently, deep transformers have proven their capability to record global interactions between different objects in the image. In this paper, we propose a new segmentation model that combines convolutional neural networks with transformers, and show that this mixture of local and global feature extraction techniques provides significant advantages in remote sensing segmentation. In addition, the proposed model includes two fusion layers that are designed to represent multi-modal inputs and output of the network efficiently. The input fusion layer extracts feature maps summarizing the relationship between image content and elevation maps (DSM). The output fusion layer uses a novel multi-task segmentation strategy where class labels are identified using class-specific feature extraction layers and loss functions. Finally, a fast-marching method is used to convert all unidentified class labels to their closest known neighbors. Our results demonstrate that the proposed methodology improves segmentation accuracy compared to state-of-the-art techniques.