Multiscale CNN With Autoencoder Regularization Joint Contextual Attention Network for SAR Image Classification

Wu, Zitong; Hou, Biao; Jiao, Licheng

doi:10.1109/tgrs.2020.3004911

Cited by 30 publications

(10 citation statements)

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“…These models showcase low complexity, but cannot be scaled for multiple numbers of classes. To enhance this performance, work in [11,12,13,14] proposes use of adaptive fuzzy learning (AFL), active ensemble deep learning (AEDL), autoencoder regularization joint contextual attention network (ARJCAN), which assists in improving classification performance for multiple datasets and scenarios. Similar models are discussed in [ 15,16,17], which propose use of Spatial & Semantic Features, Novel Attention Fully Convolutional Network Method (NAFCNN), which allow the model to augment multiple feature sets for enhancing classification performance.…”

Section: Literature Reviewmentioning

confidence: 99%

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Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

2023

IJIES

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Visual representation of synthetic images is very accurate, due to which it is difficult to differentiate them for their natural counterparts. Existing models that perform this differentiation are either very complex, or cannot be scaled for multidomain image sets. Moreover, the accuracy of these models depends directly upon type of dataset & feature sets used for training & validation purposes. To overcome these limitations, this paper proposes design of a Hybrid GWO CNN Model for identification of Synthetic Images for big data Applications. The proposed model initially extracts multidomain feature sets from input images, that includes wavelet, cosine, fourier & convolutional features. These features are processed via a grey wolf optimization (GWO) Model, that assists in improving inter-class feature variance while minimizing intra-class variance levels. The GWO Model identifies training & validation sets, thereby assisting the classification model to accurately differentiate between different image types. To perform this task, a variance-based fitness function was modelled that covers both inter-class & inter-class variance levels. This classification is performed via use of a transfer learning-based CNN Model, that extends VGG-19 for high-efficiency operations. The proposed model was tested on a large number of datasets including Synthetic Fruit, Unsplash, ESPL Synthetic Image, and Okazaki Synthetic Texture Image (OSTI) databases. Based on these datasets, accuracy, precision, computational delay, recall & AUC (Area Under the Curve) metrics were evaluated & compared with existing models. It was observed that the proposed model showcased 9.5% better accuracy, 8.3% higher precision, 6.5% better recall, and 3.9% faster performance when compared with existing models. Due to such high performance under different datasets, the proposed model is useful for large-scale synthetic-image identification use cases.

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Section: Literature Reviewmentioning

confidence: 99%

“…5. Some identification techniques are vulnerable to adversarial attacks, where a malicious actor can manipulate the input image in a way that the identification technique misclassifies it [13,15].…”

Section: Many Identification Techniques Are Dependentmentioning

confidence: 99%

Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

2023

IJIES

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“…There are also two other multidimensional and multi-head attention mechanisms [21]. Multi-head attention processes the inputs linearly in multiple subsets, and finally merges them to compute the final attention weights [58], and is especially useful when employing the attention mechanism in conjunction with CNN methods [59][60][61]. Multidimensional attention, which is mostly employed for natural language processing, computes weights based on matrix representation of the features instead of vectors [62,63].…”

Section: Attention Mechanismsmentioning

confidence: 99%

“…In addition, it was observed that most of the RNN methods were used in combination with CNN methods [76,78,124]. Generative adversarial networks (GAN) [53,125,126], Graph Neural Network (GNN) [80,82], and other DL methods including capsule network [72] and autoencoders [61] were the other DL algorithms used in 12, 5, and 4 papers, respectively. Figure 9 shows the number of papers that employed the attention mechanism for each DL algorithm.…”

Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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“…Ley et al [32] employed a Generative Adversarial Network (GAN) transcoding to transcode SAR images into the optical images, then the output layers of FCN were replaced with a classifier. Wu et al [33] designed a multi-scale convolutional neural network for pixel-wise classification, which is following an encoder-decoder architecture, and this network utilized the auto-encoder regularization branch and the contextual attention branch for learning classification information efficiently. Fang et al [34] designed a siamese U-net with sharing weights and a Fast Fourier Transform (FFT) correlation layer for SARoptical matching, and notably, the global context and local details of SAR and optical images were well retained.…”

mentioning

confidence: 99%

A Wavelet-Driven Subspace Basis Learning Network for High-Resolution Synthetic Aperture Radar Image Classification

Zhai

et al. 2023

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

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Multiscale CNN With Autoencoder Regularization Joint Contextual Attention Network for SAR Image Classification

Cited by 30 publications

References 50 publications

Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

Design of a Hybrid GWO CNN Model for Identification of Synthetic Images via Transfer Learning Process

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

A Wavelet-Driven Subspace Basis Learning Network for High-Resolution Synthetic Aperture Radar Image Classification

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