A lightweight deep learning model for classification of synthetic aperture radar images

Passah, Alicia; Kandar, Debdatta

doi:10.1016/j.ecoinf.2023.102228

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…Aiming at the problems of large number of parameters and insufficient attention to the focus region in U-Net++ network, Niu et al 26 introduced the attention mechanism on the basis of U-Net++ by removing the depth supervision and replacing the convolutional block with RegNet, which effectively reduces the number of parameters in the network and solves the problem of insufficient attention to the focus region. Alicia Passah et al 27 propose a novel, simple network model that outperforms existing models in terms of both parameter complexity and classification accuracy by drawing on the properties of the InceptionV3 and MobileNet models and combining deep and unit-separable convolution.…”

Section: Introductionmentioning

confidence: 99%

Research on land cover classification of multi-source remote sensing data based on improved U-net network

Zhang,

Roslan,

Wang

et al. 2023

Sci Rep

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In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent.

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Section: Introductionmentioning

confidence: 99%

Research on land cover classification of multi-source remote sensing data based on improved U-net network

Zhang,

Roslan,

Wang

et al. 2023

Sci Rep

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“…This process can be carried out through manual testing or automatic optimization. However, manual testing comes with several limitations, including difficulties in dealing with numerous parameters [20], complex models, extended and costly evaluations, and non-linear hyperparameter interactions [17]. Consequently, automatic hyperparameter optimization has emerged as a practical solution for numerous domain applications [18,21,22].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning: Models, Challenges, and Research Directions

Talaei Khoei,

Kaabouch

2023

Future Internet

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Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and parameter optimization. While existing surveys have shed light on these techniques, they have mainly focused on specific application domains. A notable gap that exists in current studies is the lack of a comprehensive overview of machine learning architecture and its essential phases in the cybersecurity field. To address this gap, this survey provides a holistic review of current studies in machine learning, covering techniques applicable to any domain. Models are classified into four categories: supervised, semi-supervised, unsupervised, and reinforcement learning. Each of these categories and their models are described. In addition, the survey discusses the current progress related to data pre-processing and hyperparameter tuning techniques. Moreover, this survey identifies and reviews the research gaps and key challenges that the cybersecurity field faces. By analyzing these gaps, we propose some promising research directions for the future. Ultimately, this survey aims to serve as a valuable resource for researchers interested in learning about machine learning, providing them with insights to foster innovation and progress across diverse application domains.

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Application of the domain adaptation method using a phenological classification framework for the land-cover classification of North Korea

Kim,

Jo,

Kim

et al. 2024

Ecological Informatics

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A lightweight deep learning model for classification of synthetic aperture radar images

Cited by 4 publications

References 48 publications

Research on land cover classification of multi-source remote sensing data based on improved U-net network

Research on land cover classification of multi-source remote sensing data based on improved U-net network

Machine Learning: Models, Challenges, and Research Directions

Application of the domain adaptation method using a phenological classification framework for the land-cover classification of North Korea

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