Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Xili Wang"'
Publikováno v:
Carbon Research, Vol 3, Iss 1, Pp 1-14 (2024)
Abstract As a highly developed region, Guangdong province has substantial industrial emissions. Its subtropical monsoon climate, characterized by abundant hydrothermal conditions, contributes to a substantial biomass potential. The adoption of potent
Externí odkaz:
https://doaj.org/article/7fc6241fb2c74461b7ac24d24dd0216c
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-8 (2024)
Abstract Background To evaluate the effectiveness of the computed tomographic (CT) volumetric analysis in postoperative lung function assessment and the predicting value for postoperative complications in patients who had segmentectomy for lung cance
Externí odkaz:
https://doaj.org/article/bc9150d31d5e42d9895b15e03755f29b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9360-9375 (2024)
Few-shot segmentation aims to segment a large number of unlabeled samples in the target domain, by leveraging the images and labels from the source domain as well as a few labeled samples from the target domain. This is pivotal in tackling the scarci
Externí odkaz:
https://doaj.org/article/bf80b01e7876497b82c09f77f551f71d
Autor:
Qianqian Liu, Xili Wang
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2289 (2024)
Image–text multimodal deep semantic segmentation leverages the fusion and alignment of image and text information and provides more prior knowledge for segmentation tasks. It is worth exploring image–text multimodal semantic segmentation for remo
Externí odkaz:
https://doaj.org/article/1130c44096b64aef9ef007e79f087f22
Autor:
Qi Li, Yanxin Tang, Dubin Dong, Xili Wang, Xuqiao Wu, Saima Gul, Yaqian Li, Xiaocui Xie, Dan Liu, Weijie Xu
Publikováno v:
Agriculture, Vol 14, Iss 6, p 867 (2024)
Phytoremediation is considered an effective strategy for remediation of heavy-metal-contaminated soil in mining areas. However, single-species plants cannot reach the highest potential for uptake of heavy metals due to inhibition of their growth by h
Externí odkaz:
https://doaj.org/article/8b3c8767c1d640368c317109d86974aa
Autor:
Xili Wang, Zhengyin Liang
Publikováno v:
IET Image Processing, Vol 17, Iss 1, Pp 256-273 (2023)
Abstract Hyperspectral images (HSIs) contain hundreds of continuous spectral bands and are rich in spectral‐spatial information. In terms of HSIs’ classification, traditional convolutional neural networks (CNNs) extract features based on HSI's sp
Externí odkaz:
https://doaj.org/article/6e573f7b06e74e03a340963f687aea70
Publikováno v:
IEEE Access, Vol 8, Pp 221225-221234 (2020)
Most graph-based semi-supervised classification methods do not perform well in hyperspectral image classification tasks due to their high complexity and other limitations. This paper proposes a label propagation semi-supervised classification algorit
Externí odkaz:
https://doaj.org/article/096b1e797e7b455fa62100c0f318e381
Publikováno v:
IEEE Access, Vol 7, Pp 104500-104513 (2019)
Micro Doppler analysis of spin stabilized objects is of a great significance for attitude estimation and recognition of space targets. In practice, the radar cannot dwell on one target in a long interval continuously. In this paper, we propose a nove
Externí odkaz:
https://doaj.org/article/d342578802d04faea603a5d1c38faf0a
Publikováno v:
Sensors, Vol 22, Iss 15, p 5735 (2022)
Deep learning techniques have brought substantial performance gains to remote sensing image classification. Among them, convolutional neural networks (CNN) can extract rich spatial and spectral features from hyperspectral images in a short-range regi
Externí odkaz:
https://doaj.org/article/1847b6fe33cd44f5804144dbbdb71fa6
Publikováno v:
IEEE Access, Vol 5, Pp 16618-16634 (2017)
K-nearest neighbor rule (KNN) and sparse representation (SR) are widely used algorithms in pattern classification. In this paper, we propose two new nearest neighbor classification methods, in which the novel weighted voting methods are developed for
Externí odkaz:
https://doaj.org/article/7c755226275c486c821709c44c064290