2022
DOI: 10.3390/app12094159
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Use of a DNN-Based Image Translator with Edge Enhancement Technique to Estimate Correspondence between SAR and Optical Images

Abstract: In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by co… Show more

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Cited by 3 publications
(1 citation statement)
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“…The first paper is authored by H. Toriya and A. Dewan et al, who primarily explore the key point matching problem in image features. They propose using a deep neural network (DNN) to construct an image translator and introduce a new edge enhancement filter methodology within the conditional generative adversarial network (cGAN) structure to tackle this issue [7]. The second paper, written by Z. Wei and Z. Zhang, describes a network built on multi-level strip pooling and a feature enhancement module (MSPFE-Net).…”
Section: Deep Learning Approaches In Remote Sensing Image Classificationmentioning
confidence: 99%
“…The first paper is authored by H. Toriya and A. Dewan et al, who primarily explore the key point matching problem in image features. They propose using a deep neural network (DNN) to construct an image translator and introduce a new edge enhancement filter methodology within the conditional generative adversarial network (cGAN) structure to tackle this issue [7]. The second paper, written by Z. Wei and Z. Zhang, describes a network built on multi-level strip pooling and a feature enhancement module (MSPFE-Net).…”
Section: Deep Learning Approaches In Remote Sensing Image Classificationmentioning
confidence: 99%