Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review

Khlifi, Manel Khazri; Boulila, Wadii; Farah, Imed Riadh

doi:10.1016/j.cosrev.2023.100596

Cited by 5 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The limitation of GSANet is the inability to accurately depict the edge contours of salient objects in some complex scenes, resulting in detected edges that are blurred or fragmented. To address this issue, we will consider utilizing graph convolutional networks [ 48 ] in future research to capture the spatial relationships within remote sensing images and further enhance edge features.…”

Section: Discussionmentioning

confidence: 99%

Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images

Li,

Chen,

Yang

et al. 2024

Entropy

View full text Add to dashboard Cite

Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% Sα, 98.36% Eξ, and 89.37% Fβ on the EORSSD dataset.

show abstract

Section: Discussionmentioning

confidence: 99%

Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images

Li,

Chen,

Yang

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

“…In general, there is little to no correlation between the traits of different categories. For instance, a user’s age and gender are examples of natural categories describing the person or object, and age and sexual orientation are unrelated ( Khlifi, Boulila & Farah, 2023 ).…”

Section: System Modelmentioning

confidence: 99%

Advertisement design in dynamic interactive scenarios using DeepFM and long short-term memory (LSTM)

Zeng,

Asif

2024

PeerJ Computer Science

View full text Add to dashboard Cite

This article addresses the evolving landscape of data advertising within network-based new media, seeking to mitigate the accuracy limitations prevalent in traditional film and television advertising evaluations. To overcome these challenges, a novel data-driven nonlinear dynamic neural network planning approach is proposed. Its primary objective is to augment the real-time evaluation precision and accuracy of film and television advertising in the dynamic interactive realm of network media. The methodology primarily revolves around formulating a design model for visual advertising in film and television, customized for the dynamic interactive milieu of network media. Leveraging DeepFM+long short-term memory (LSTM) modules in deep learning neural networks, the article embarks on constructing a comprehensive information statistics and data interest model derived from two public datasets. It further engages in feature engineering for visual advertising, crafting self-learning association rules that guide the data-driven design process and system flow. The article concludes by benchmarking the proposed visual neural network model against other models, using F1 and root mean square error (RMSE) metrics for evaluation. The findings affirm that the proposed model, capable of handling dynamic interactions among images, visual text, and more, excels in capturing nonlinear and feature-mining aspects. It exhibits commendable robustness and generalization capabilities within various contexts.

show abstract

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Bera,

Bhattacharjee,

Krejcar

2024

Sci Rep

View full text Add to dashboard Cite

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40$$\times $$ × : 95.50%, and BreakHis 100$$\times $$ × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.

show abstract

Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications — A comprehensive review

Cited by 5 publications

References 66 publications

Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images

Global Semantic-Sense Aggregation Network for Salient Object Detection in Remote Sensing Images

Advertisement design in dynamic interactive scenarios using DeepFM and long short-term memory (LSTM)

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Contact Info

Product

Resources

About