Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm

Vitale, Sergio; Ferraioli, Giampaolo; Pascazio, Vito

doi:10.1109/igarss39084.2020.9323909

Cited by 3 publications

(2 citation statements)

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“…Deeper networks allow us to extract more features and to add more abstractions, facilitating the exploitation of the data and the network generalization. The depth has been set experimentally: in [46], it has been proved that a deeper network gives better results.…”

Section: F Contributionmentioning

confidence: 99%

Multi-Objective CNN-Based Algorithm for SAR Despeckling

Vitale

Ferraioli

Pascazio

2021

IEEE Trans. Geosci. Remote Sensing

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Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous.

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Section: F Contributionmentioning

confidence: 99%

Multi-Objective CNN-Based Algorithm for SAR Despeckling

Vitale

Ferraioli

Pascazio

2021

IEEE Trans. Geosci. Remote Sensing

Self Cite

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“…For the development of the theory and technology of SAR, researches have been carried out in many fields of SAR, such as SAR image despeckling [3], super-resolution [4], target detection, classification, recognition, and multi-sensor image fusion [5], [6]. All these researches are driven by SAR data among which the SAR target images are the most important.…”

mentioning

confidence: 99%

SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network

Wang

Pei

Liu

et al. 2022

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

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Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this paper, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: generator, discriminator, and predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate SAR target image. Then a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the researches of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation.

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