Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Jinnian Wang"'
Publikováno v:
Geographies, Vol 3, Iss 2, Pp 246-267 (2023)
Monitoring CO2 concentrations is believed to be an effective measure for assisting in the control of greenhouse gas emissions. Satellite measurements compensate for the sparse and uneven spatial distribution of ground observation stations, allowing f
Externí odkaz:
https://doaj.org/article/c65670365e37454abb5c2ae174c0b4a6
Autor:
Yiming Zhao, Xujun Mo, Hao Wang, Jiangyong Li, Daocheng Gong, Dakang Wang, Qinqin Li, Yunfeng Liu, Xiaoting Liu, Jinnian Wang, Boguang Wang
Publikováno v:
Remote Sensing, Vol 15, Iss 16, p 3998 (2023)
Formaldehyde (HCHO) plays an important role in atmospheric photochemical reactions. Comparative studies between ground-based and satellite observations are necessary to assess and promote the potential use of column HCHO as a proxy for surface HCHO a
Externí odkaz:
https://doaj.org/article/e5acb31b76e7411982c37340ff4e79aa
Publikováno v:
Remote Sensing, Vol 15, Iss 15, p 3875 (2023)
Forests are the most important carbon reservoirs on land, and forest carbon sinks can effectively reduce atmospheric CO2 concentrations and mitigate climate change. In recent years, various satellites have been launched that provide opportunities for
Externí odkaz:
https://doaj.org/article/2fd9bb5c9c6e4ec78791442ce495bf74
Autor:
Yibo Wang, Xia Zhang, Changping Huang, Wenchao Qi, Jinnian Wang, Xiankun Yang, Songtao Ding, Shiyu Tao
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3269 (2023)
Satellite hyperspectral imagery is an important data source for large-scale refined land cover classification and mapping, but the high spatial heterogeneity and spectral variability at low spatial resolution and the high computation cost for massive
Externí odkaz:
https://doaj.org/article/36222dbcdb4f4b088b2acb13d158bf5f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 6339-6352 (2021)
The background dictionary used in the hyperspectral images anomaly detection based on low-rank and sparse representation (LRASR) contains both target information and background information which will result in low detection accuracy. In response to t
Externí odkaz:
https://doaj.org/article/3e53341de3da478bbfc463f3a22e406e
Autor:
Xiaoyuan Zhang, Kai Liu, Shudong Wang, Taixia Wu, Xueke Li, Jinnian Wang, Dacheng Wang, Haitao Zhu, Chang Tan, Yuhe Ji
Publikováno v:
Ecological Indicators, Vol 135, Iss , Pp 108586- (2022)
Drought, water shortage, and anthropogenic disturbance bring about serious ecological issues in the Yellow River Basin. In recent decades, the “Grain-for-Green” project, wind-sand control, and water ecological civilization construction are major
Externí odkaz:
https://doaj.org/article/6b0746a091694648947aa047d317c640
Publikováno v:
Remote Sensing, Vol 14, Iss 21, p 5415 (2022)
Natural imagery segmentation has been transferred to land cover classification in remote sensing imagery with excellent performance. However, two key issues have been overlooked in the transfer process: (1) some objects were easily overwhelmed by the
Externí odkaz:
https://doaj.org/article/462e47389fac4e008c6b598db5e4084d
Autor:
Tao, Yibo Wang, Xia Zhang, Changping Huang, Wenchao Qi, Jinnian Wang, Xiankun Yang, Songtao Ding, Shiyu
Publikováno v:
Remote Sensing; Volume 15; Issue 13; Pages: 3269
Satellite hyperspectral imagery is an important data source for large-scale refined land cover classification and mapping, but the high spatial heterogeneity and spectral variability at low spatial resolution and the high computation cost for massive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::ab7790be1539adb76eac90ad8cd69239
Publikováno v:
Sustainability; Volume 15; Issue 9; Pages: 7659
Unlocking the secrets of habitable urban areas is crucial to improve the quality of life for urban dwellers. Accurate assessment of the ever-changing dynamics of a modern metropolis remains a challenging task. Previous studies have failed to reveal t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3babab5119b17f5262a290b75c46a49
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 6339-6352 (2021)
The background dictionary used in the hyperspectral images anomaly detection based on low-rank and sparse representation (LRASR) contains both target information and background information which will result in low detection accuracy. In response to t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d0c204fab0f893ac651db0c5c2471a6
https://ieeexplore.ieee.org/document/9448438/
https://ieeexplore.ieee.org/document/9448438/