Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Zhang, Yake; Liu, Fang; Jiao, Licheng; Yang, Shuyuan; Yang, Meijuan

doi:10.1109/jstars.2020.3024002

Cited by 11 publications

(5 citation statements)

References 54 publications

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“…The reviewed techniques used for identification of synthetic images have limitations, some of which include, 1. The current techniques used for identifying synthetic images are not always effective in distinguishing between real and synthetic images [1][2][3]. Some of the techniques are based on statistical analysis of pixel-level features, which can be easily manipulated by sophisticated generators [4].…”

Section: Issues With Existing Techniquesmentioning

confidence: 99%

“…These features are processed via design of accurate classifiers that can categorize images based on these features, development of post-processing models that can pre-empt presence of synthetic regions in natural image sets. A typical synthetic image identification model [1] is depicted in Fig. 1, wherein image processing operations including instance matching, image rendering, partial modelling, feature adaptation, class validation, and continuous learning can be observed.…”

Section: Introductionmentioning

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: Issues With Existing Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2023

IJIES

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“…Being a form of radar, synthetic aperture radar (SAR) is capable of high-resolution remote sensing. Moreover, due to its all-time, all-weather, and large-scale observation capabilities, the SAR system can provide detailed information about the monitored region and, thus, plays an important role in many applications of both military and civilian fields [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. For example, the SAR can gather systematically high-quality data of a city and help build a smart city [18].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Classification with Few Labeled Data Using Semisupervised Learning

Zhang

et al. 2023

Wireless Communications and Mobile Computing

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Synthetic aperture radar (SAR) as an imaging radar is capable of high-resolution remote sensing, independent of flight altitude, and independent of weather. Traditional SAR ship image classification tends to extract features manually. It relies too much on expert experience and is sensitive to the scale of SAR images. Recently, with the development of deep learning, deep neural networks such as convolutional neural networks are widely used to complete feature extraction and classification tasks, which improves algorithm accuracy and normalization capabilities to a large extent. However, deep learning requires a large number of labeled samples, and the vast bulk of SAR images are unlabeled. Therefore, the classification accuracy of deep neural networks is limited. To tackle the problem, we propose a semisupervised learning-based SAR image classification method considering that only few labeled images are available. The proposed method can train the classification model using both labeled and unlabeled samples. Moreover, we improve the unsupervised data augmentation (UDA) strategy by designing a symmetric function for unsupervised loss calculation. Experiments are carried out on the OpenSARShip dataset, and results show that the proposed method reaches a much higher accuracy than the original UDA.

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“…Wang et al [13] proposed a random simulation classification algorithm of feature space indicators, which extended the statistical method from two-dimensional geographical space to m-dimensional image feature space to deduce the variation function of feature space indicators, and realized the accurate classification of land cover and use types in Duolun County, Inner Mongolia, and Huangfengqiao forest farm, Hunan, China. Zhang et al [14] proposed a discriminant sketch subject model with structural constraints for SAR image classification.…”

Section: Introductionmentioning

confidence: 99%

Land Use Classification Method of Remote Sensing Images for Urban and Rural Planning Monitoring Using Deep Learning

Xie

Kang

Yan

et al. 2022

Scientific Programming

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Aiming at the problems that most existing segmentation methods are difficult to deal with the imbalance of remote sensing image distribution and the overlap of segmentation target edges, a land use classification method of remote sensing image for urban and rural planning monitoring based on deep learning is proposed. Firstly, the U-Net is improved by pooling index upsampling and dimension superposition. The improvement can not only extract high-level abstract features but also extract low-level detail features, so as to reduce the loss of image edge information in the process of deconvolution. Then, the batch normalization and scaling exponential linear unit (SeLU) are used to improve the U-Net model. Finally, the improved U-Net model is applied to the classification of remote sensing images of land use types to realize dynamic monitoring. The experimental analysis of the proposed method based on TensorFlow deep learning framework shows that its total accuracy exceeds 94%. The segmentation effect of land use types in remote sensing images is good.

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Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Cited by 11 publications

References 54 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

Remote Sensing Image Classification with Few Labeled Data Using Semisupervised Learning

Land Use Classification Method of Remote Sensing Images for Urban and Rural Planning Monitoring Using Deep Learning

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