Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, the integration of graph convolutional networks (GCNs) in forest fire detection remains relatively underexplored. In this context, we introduce a novel superpixel-based graph convolutional network (SCGCN) for forest fire image segmentation. Our proposed method utilizes superpixels to transform images into a graph structure, thereby reinterpreting the image segmentation challenge as a node classification task. Additionally, we transition the spatial graph convolution operation to a GraphSAGE graph convolution mechanism, mitigating the class imbalance issue and enhancing the network’s versatility. We incorporate an innovative loss function to contend with the inconsistencies in pixel dimensions within superpixel clusters. The efficacy of our technique is validated on two different forest fire datasets, demonstrating superior performance compared to four alternative segmentation methodologies.
Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
A novel improved SLIC superpixel segmentation algorithm
IEEE | 2022
|Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation
British Library Online Contents | 2016
|Superpixel-based class-semantic texton occurrences for natural roadside vegetation segmentation
British Library Online Contents | 2017
|Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation
British Library Online Contents | 2016
|Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation
British Library Online Contents | 2016
|