Zobrazeno 1 - 10
of 124
pro vyhledávání: '"sample augmentation"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 10158-10173 (2024)
Collaborative representation (CR) models have been widely used in hyperspectral image (HSI) classification tasks. However, most CR classification models lack stability and generalization when targeting small samples as well as spatial homogeneity and
Externí odkaz:
https://doaj.org/article/4460a238b9794000acf341fef282695e
Publikováno v:
Defence Technology, Vol 28, Iss , Pp 74-86 (2023)
Traditional object detectors based on deep learning rely on plenty of labeled samples, which are expensive to obtain. Few-shot object detection (FSOD) attempts to solve this problem, learning detection objects from a few labeled samples, but the perf
Externí odkaz:
https://doaj.org/article/8a9b1b9107554ec5a890a1ae81a4bc5e
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 8031 (2024)
The utilization of deep learning algorithms for side-scan sonar target detection is impeded by the restricted quantity and representativeness of side-scan sonar (SSS) samples. To address this issue, this paper proposes a method for image augmentation
Externí odkaz:
https://doaj.org/article/a4596d16d83f4222be7cd67dc4edd879
Publikováno v:
Sensors, Vol 24, Iss 15, p 5060 (2024)
Side-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition. To expand the number of representative side-scan sonar
Externí odkaz:
https://doaj.org/article/f99abb3d9ba849549e3795754bae49d0
Autor:
Yunze Zang, Yuean Qiu, Xuehong Chen, Jin Chen, Wei Yang, Yifei Liu, Longkang Peng, Miaogen Shen, Xin Cao
Publikováno v:
GIScience & Remote Sensing, Vol 60, Iss 1 (2023)
Rapeseed mapping is important for national food security and government regulation of land use. Various methods, including empirical index-based and machine learning-based methods, have been developed to identify rapeseed using remote sensing. Empiri
Externí odkaz:
https://doaj.org/article/d86f927a0d9f48fc85319fbc16c299b4
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 6393-6410 (2023)
Side-scan sonar (SSS) image sample augmentation plays an important role in improving the effect of deep-learning-based underwater target detection. However, the existing sample augmentation methods for cross-domain conversion always result in weak re
Externí odkaz:
https://doaj.org/article/6c39a83b2cf04fc0a2881aedc48f2d37
Publikováno v:
Remote Sensing, Vol 15, Iss 24, p 5654 (2023)
With the widespread application and functional complexity of deep neural networks (DNNs), the demand for training samples is increasing. This elevated requirement also extends to DNN-based SAR object detection. Most public SAR object detection datase
Externí odkaz:
https://doaj.org/article/50dd218321ca4afe9900b2b8bb147d08
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