Remote sensing image scene classification by transfer learning to augment the accuracy

Thirumaladevi, S.; Swamy, K. Veera; Sailaja, M.

doi:10.1016/j.measen.2022.100645

Cited by 12 publications

(2 citation statements)

References 12 publications

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“…A flatten layer collapses the spatial dimensions into one-dimensional features. Two FC layers were set to select the features extracted from the convolutional layer [32]. The two FC layers had 128 and 64 nodes, respectively, with a Rectified Linear Unit (ReLU) activation function and a dropout of 0.3, to show the grades as the result of the softmax function.…”

Section: Architecture Of Convolutional Neural Networkmentioning

confidence: 99%

Automated Grading of Red Ginseng Using DenseNet121 and Image Preprocessing Techniques

Kim,

Kim

et al. 2023

Agronomy

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Red ginseng is a steamed and dried ginseng that has more functional properties and a longer shelf-life. Red ginseng is graded by appearance and inner quality. However, this conventional process has a high cost in terms of time and human resources, and has the disadvantage of subjective assessment results. Therefore, the convolutional neural network (CNN) method was proposed to automate the grading process of red ginseng and optimize the preprocessing method, select an accurate and efficient deep learning model, and to explore the feasibility of rating discrimination solely based on external quality information, without considering internal quality characteristics. In this study, the effect of five distinct preprocessing methods, including RGB, binary, gray, contrast-limited adaptive histogram equalization (CLAHE), and Gaussian blur, on the rating accuracy of red ginseng images was investigated. Furthermore, a comparative analysis was conducted on the performance of four different models, consisting of one CNN model and three transfer learning models, which were VGG19, MobileNet, and DenseNet121. Among them, DenseNet121 with CLAHE preprocessing reported the best performance; its accuracy in the Dataset 2 test set was 95.11%. This finding suggests that deep learning techniques can provide an objective and efficient solution for the grading process of red ginseng without an inner quality inspection.

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Section: Architecture Of Convolutional Neural Networkmentioning

confidence: 99%

Automated Grading of Red Ginseng Using DenseNet121 and Image Preprocessing Techniques

Kim,

Kim

et al. 2023

Agronomy

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show abstract

“…In addition to that, the performance has been improved significantly after employing the optimization process [35,36]. Few latest studies also focused on deep learning for the agri-yield classification using remote sensing images [20,[37][38][39][40][41]. Overall, it is a complex process that first fuses the features of both models and then selects the best of them; therefore, it is important to consider a more optimized approach, such as network-level fusion.…”

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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Enhancing Remote Sensing Scene Classification with Channel-Spatial CNN (CS-CNN)

Agnes,

Pedada,

Sambangi

et al. 2024

Intelligent Systems Design and Applications

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Remote sensing image scene classification by transfer learning to augment the accuracy

Cited by 12 publications

References 12 publications

Automated Grading of Red Ginseng Using DenseNet121 and Image Preprocessing Techniques

Automated Grading of Red Ginseng Using DenseNet121 and Image Preprocessing Techniques

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

Enhancing Remote Sensing Scene Classification with Channel-Spatial CNN (CS-CNN)

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