Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Jianda CHENG"'
Publikováno v:
Sensors, Vol 24, Iss 15, p 5006 (2024)
Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic ap
Externí odkaz:
https://doaj.org/article/b23b14b8991e4d0690d9d55a6addddd4
Publikováno v:
Leida xuebao, Vol 11, Iss 6, Pp 1081-1097 (2022)
Synthetic Aperture Radar (SAR) image registration has recently been one of the most challenging tasks because of speckle noise, geometric distortion and nonlinear radiation differences between SAR images. The repeatability of keypoints and the effect
Externí odkaz:
https://doaj.org/article/1861adf7dae442068680aa1266efea38
Publikováno v:
Zhongguo Jianchuan Yanjiu, Vol 16, Iss 6, Pp 45-51 (2021)
ObjectivesNaval ship systems such as the hull structure, weapons equipment and power equipment will deteriorate during their service life. Thus, a ship maintenance strategy based on the actual deterioration state is essential for ensuring the safety
Externí odkaz:
https://doaj.org/article/01400356c9cf4423bcfcfc323e61207e
Publikováno v:
IEEE Access, Vol 8, Pp 173826-173837 (2020)
Decision tree method has been applied to POLSAR image classification, due to its capability to interpret the scattering characteristics as well as good classification accuracy. Compared with popular machine learning classifiers, decision tree approac
Externí odkaz:
https://doaj.org/article/7077f84897d54790a3983521e82ed56b
Publikováno v:
Remote Sensing, Vol 14, Iss 12, p 2832 (2022)
While the detection of offshore ships in synthetic aperture radar (SAR) images has been widely studied, inshore ship detection remains a challenging task. Due to the influence of speckle noise and the high similarity between onshore buildings and ins
Externí odkaz:
https://doaj.org/article/20c953ee4fc547b1bed6edc1927695ef
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 899 (2022)
Most of the existing synthetic aperture radar (SAR) image superpixel generation methods are designed based on the raw SAR images or artificially designed features. However, such methods have the following limitations: (1) SAR images are severely affe
Externí odkaz:
https://doaj.org/article/ba6670f39b564837ab261d5564d829b5
Publikováno v:
Remote Sensing, Vol 13, Iss 16, p 3132 (2021)
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image classific
Externí odkaz:
https://doaj.org/article/98acc0114ce04daa84671318a50f99fe
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 20:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98cd91c4bdbea852c63404d0efacc355
https://doi.org/10.1109/lgrs.2023.3242081
https://doi.org/10.1109/lgrs.2023.3242081
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::965230e5f2cd413393e473c8d5f82ceb
https://doi.org/10.1109/tgrs.2022.3211858
https://doi.org/10.1109/tgrs.2022.3211858
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-14
Convolutional neural networks (CNNs) have demonstrated impressive ability to achieve promising results in PolSAR image classification. However, the traditional CNN performs convolution on local square regions with fixed sizes. The selection of these
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49a9960d40ff6ea12df45b5cf058674b
https://doi.org/10.1109/tgrs.2021.3079438
https://doi.org/10.1109/tgrs.2021.3079438