CDAU-Net: A Novel CoordConv-Integrated Deep Dual Cross Attention Mechanism for Enhanced Road Extraction in Remote Sensing Imagery

• الحاوية / القاعدة: Remote Sensing
• المؤلفون الرئيسيون: Anchao Yin, Chao Ren, Weiting Yue, Hongjuan Shao, Xiaoqin Xue
• التنسيق: مقال
• اللغة: الإنجليزية
• منشور في: MDPI AG 2023-10-01
• الموضوعات: remote sensing image analysis; road extraction; CDAU-Net; CoordConv; convolutions; Deep Dual Cross Attention (DDCA)
• الوصول للمادة أونلاين: https://www.mdpi.com/2072-4292/15/20/4914

In the realm of remote sensing image analysis, the task of road extraction poses significant complexities, especially in the context of intricate scenes and diminutive targets. In response to these challenges, we have developed a novel deep learning network, christened CDAU-Net, designed to discern and delineate these features with enhanced precision. This network takes its structural inspiration from the fundamental architecture of U-Net while introducing innovative enhancements: we have integrated CoordConv convolutions into both the initial layer of the U-Net encoder and the terminal layer of the decoder, thereby facilitating a more efficacious processing of spatial information inherent in remote sensing images. Moreover, we have devised a unique mechanism termed the Deep Dual Cross Attention (DDCA), purposed to capture long-range dependencies within images—a critical factor in remote sensing image analysis. Our network replaces the skip-connection component of the U-Net with this newly designed mechanism, dealing with feature maps of the first four scales in the encoder and generating four corresponding outputs. These outputs are subsequently linked with the decoder stage to further capture the remote dependencies present within the remote sensing imagery. We have subjected CDAU-Net to extensive empirical validation, including testing on the Massachusetts Road Dataset and DeepGlobe Road Dataset. Both datasets encompass a diverse range of complex road scenes, making them ideal for evaluating the performance of road extraction algorithms. The experimental results showcase that whether in terms of accuracy, recall rate, or Intersection over Union (IoU) metrics, the CDAU-Net outperforms existing state-of-the-art methods in the task of road extraction. These findings substantiate the effectiveness and superiority of our approach in handling complex scenes and small targets, as well as in capturing long-range dependencies in remote sensing imagery. In sum, the design of CDAU-Net not only enhances the accuracy of road extraction but also presents new perspectives and possibilities for deep learning analysis of remote sensing imagery.