CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

Guo, Ningbo; Jiang, Mingyong; Gao, Lijing; Tang, Yizhuo; Han, Jinwei; Chen, Xiangning

doi:10.3390/s23177514

Cited by 3 publications

(2 citation statements)

References 64 publications

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“…A few sample images are illustrated in Figure 1. Several techniques were introduced in the literature to classify land cover from RS images [6,7]. The presented techniques are based on supervised learning and unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Albarakati,

Khan,

Hamza

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop monitoring, and climate change adaptation, can be obtained from remote sensing and artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture for land use and land cover classification using remote sensing (RS) data. A new contrast enhancement equation has been proposed and utilized in the proposed architecture for the data augmentation. After that, a fused Self-Attention CNN architecture was proposed. The proposed architecture initially consists of two custom models named IBNR-65 and Densenet-64. Both models have been designed based on the inverted bottleneck residual mechanism and Dense Blocks. After that, both models were fused using a depth-wise concatenation and append a Self-Attention layer for deep features extraction. After that, we trained the model and performed classification using neural network (NN) classifiers. The results obtained from the NN classifiers are insufficient; therefore, we implemented a Bayesian Optimization and fine-tuned the hyperparameters of NN. In addition, we proposed a Quantum Hippopotamus Optimization Algorithm for the best feature selection. The selected features are finally classified using fine-tuned NN classifiers and obtained improved accuracy of 98.20, 89.50, and 91.70%, and the highest precision rate is 98.23, recall is 98.20, and f1-score is 98.21 respectively, for SIRI-WHU, EuroSAT, and NWPU datasets. Moreover, a detailed ablation study was conducted, and the performance was compared with SOTA. The proposed architecture shows

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Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

Albarakati,

Khan,

Hamza

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The fundamental purpose of utilizing satellite remote sensing data for monitoring the production status of shipyards lies in determining whether the shipyard is engaged in production activities at the time of imaging, based on its distinctive characteristics. In simpler scenarios, discerning the state of a scene typically involves integrating relatively straightforward middle-and low-level semantics with machine learning techniques [35][36][37]. In more complex scenes, two processing approaches can be employed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Qin,

Song,

Zhu

et al. 2023

Remote Sensing

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Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.

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BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties

Sagar,

Tanveer,

Chen

et al. 2024

Remote Sensing

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Remote sensing stands as a fundamental technique in contemporary environmental monitoring, facilitating extensive data collection and offering invaluable insights into the dynamic nature of the Earth’s surface. The advent of deep learning, particularly convolutional neural networks (CNNs), has further revolutionized this domain by enhancing scene understanding. However, despite the advancements, traditional CNN methodologies face challenges such as overfitting in imbalanced datasets and a lack of precise uncertainty quantification, crucial for extracting meaningful insights and enhancing the precision of remote sensing techniques. Addressing these critical issues, this study introduces BayesNet, a Bayesian neural network (BNN)-driven CNN model designed to normalize and estimate uncertainties, particularly aleatoric and epistemic, in remote sensing datasets. BayesNet integrates a novel channel–spatial attention module to refine feature extraction processes in remote sensing imagery, thereby ensuring a robust analysis of complex scenes. BayesNet was trained on four widely recognized unmanned aerial vehicle (UAV)-based remote sensing datasets, UCM21, RSSCN7, AID, and NWPU, and demonstrated good performance, achieving accuracies of 99.99%, 97.30%, 97.57%, and 95.44%, respectively. Notably, it has showcased superior performance over existing models in the AID, NWPU, and UCM21 datasets, with enhancements of 0.03%, 0.54%, and 0.23%, respectively. This improvement is significant in the context of complex scene classification of remote sensing images, where even slight improvements mark substantial progress against complex and highly optimized benchmarks. Moreover, a self-prepared remote sensing testing dataset is also introduced to test BayesNet against unseen data, and it achieved an accuracy of 96.39%, which showcases the effectiveness of the BayesNet in scene classification tasks.

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CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

Cited by 3 publications

References 64 publications

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties

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