Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers

TL;DR

This paper introduces a novel segmentation model combining CNNs and transformers for high-resolution remote sensing images, enhancing accuracy by integrating multi-modal data and multi-task strategies.

Contribution

It proposes a new hybrid CNN-transformer model with fusion layers for multi-modal input and multi-task segmentation, improving remote sensing image analysis.

Findings

Improved segmentation accuracy over state-of-the-art methods

Effective multi-modal data integration via fusion layers

Enhanced global and local feature learning

Abstract

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.