A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images

Abstract

Wetlands, situated at the vital intersection of terrestrial and aquatic ecosystems, are pivotal in preserving global biodiversity and maintaining environmental equilibrium. The escalating trend of global urbanization necessitates the utilization of high-resolution satellite imagery for accurate wetland delineation, which is essential for establishing efficacious conservation strategies. This study focuses on the wetlands of Macau, characterized by distinctive coastal and urban features. A noteworthy enhancement in this study is the integration of the Coordinate Attention mechanism with the RegNet model, forming the CA-RegNet. This combined model demonstrates superior performance, outdoing previous Macau wetlands segmentation studies that used ResNet, evidenced by an approximate rise of 2.7% in overall accuracy (OA), 4.0% in the Kappa coefficient, 1.9% in the mAcc, and 0.5% in the mIoU. Visual evaluations of the segmentation results reinforce the competence of the CA-RegNet model in precisely demarcating coastal wetlands and Saiwan Lake, thereby overcoming the former constraints of ResNet and underscoring the robustness and innovation of this study.

An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

“…For a more comprehensive analysis, User's Accuracy (UA, or Recall), Producer's Accuracy (PA, or Precision), and the F1-Score are employed to examine the precision of each category within the models. UA evaluates the likelihood that a pixel classified in the map/image accurately represents its real-world class, highlighting the model's precision in identifying specific classes-a key metric for tasks requiring accurate class identification [37]. UA is calculated as the ratio of correct predictions (TP) to all predictions for that class (TP and FP): UA = TP/(TP + FP)…”

“…The SE-RegNet model embodies a cutting-edge architectural integration, merging the strengths of Squeeze-and-Excitation (SE) blocks with the efficient RegNet framework, designed for precise and effective image classification tasks [36]. SE blocks, which adaptively recalibrate channel-wise feature responses, boost the network's representational capacity by methodically capturing interdependencies among channels, achieving notable performance enhancements with minimal additional computational demand [37]. RegNet, recognized for its straightforwardness and scalability, offers a modular structure that can be readily scaled and customized for various applications.…”