A deep learning based framework for remote sensing image ground object segmentation

Dong, Xingjun; Zhang, Changsheng; Fang, Lei; Yan, Yuxiao

doi:10.1016/j.asoc.2022.109695

Cited by 10 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have also been researches like Multi-Feature Network (MFNet) [17] introducing a multi-feature learning algorithm, incorporating high-level, low-level features and class discriminative features, reducing the confusion between the classes. The authors in [18] have also presented a technique to reduce inter-class confusion. A three-stage mechanism was presented, featuring Image Block Segmentation (IBS) and Superpixel Cluster (SPC) as its main components.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

Boulila,

Ghandorh,

Masood

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

“… Struggles with generalization across diverse datasets. [18] Three-stage mechanism for class confusion Superpixel clustering and block segmentation. Improves boundaries and computational efficiency.…”

Section: Related Workmentioning

confidence: 99%

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

Boulila,

Ghandorh,

Masood

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…In terms of RS data, extracting information from RS imagery data remains a challenge. The following factors contributes to inaccurate RS classification, namely the complexities inherent in deciphering intricate spatial and spectral patterns within RS Imagery [13,150], the challenge of handling diverse distributions of ground objects with variations within the same class [26] and significant intra-class and limited inter-class pixel differences [151][152][153]. Other contributing factors include data complexity, geographical time difference [154], foggy conditions [155] and data acquisition errors [147].…”

Section: Analysis Of Rs Imagesmentioning

confidence: 99%

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

Ajibola,

Cabral

2024

Preprint

View full text Add to dashboard Cite

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen &amp; Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

show abstract

“…Superpixels, a technique for segmenting images, partition the image into contiguous regions exhibiting similar texture and color characteristics. This method not only provides precise edge information but also captures the object details effectively [2,20]. Several commonly used superpixel segmentation algorithms include simple linear iterative clustering (SLIC) [21], superpixels extracted via energy-driven sampling (SEEDS) [22], and superpixel segmentation using Gaussian mixture models (GMMSP) [23].…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Mu,

Ou,

Chen

et al. 2024

Drones

View full text Add to dashboard Cite

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.

show abstract

A deep learning based framework for remote sensing image ground object segmentation

Cited by 10 publications

References 38 publications

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

A transformer-based approach empowered by a self-attention technique for semantic segmentation in remote sensing

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

Contact Info

Product

Resources

About