Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Lena Chang"'
Autor:
Lena Chang, Yi-Ting Chen
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8139-8152 (2024)
This study improved the shoreline detection performance based on the U-Net model by combining Sentinel-1 synthetic aperture radar (SAR) and digital elevation model (DEM) data. The U-Net network was first modified to enhance feature extraction by usin
Externí odkaz:
https://doaj.org/article/8442dbad1dc44a49bcd1b119486cb44b
Publikováno v:
Sensors, Vol 24, Iss 20, p 6768 (2024)
This study proposed an improved full-scale aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas using synthetic aperture radar (SAR) images. The convolutional block attention module (CBAM) in the F
Externí odkaz:
https://doaj.org/article/28035c8d22e346aba18c7402cd03a223
Publikováno v:
Sensors, Vol 24, Iss 12, p 3724 (2024)
Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneou
Externí odkaz:
https://doaj.org/article/482e08f4ef16476ab30ecac5904a949e
Publikováno v:
IEEE Access, Vol 8, Pp 106910-106923 (2020)
Recent studies have shown that wireless mesh networks (WMNs) can be cheap, reliable, and efficient solutions for Internet of Things (IoTs) applications and connected devices. However, the increase in the size of the WMNs could lead to a degradation i
Externí odkaz:
https://doaj.org/article/bf221fd5960d42cd84d23f14ab855fc2
Publikováno v:
Remote Sensing, Vol 14, Iss 20, p 5135 (2022)
Climate change and global warming lead to changes in the sea level and shoreline, which pose a huge threat to island regions. Therefore, it is important to effectively detect the shoreline changes. Taiwan is a typical island, located at the junction
Externí odkaz:
https://doaj.org/article/61a9f234ee85403a82f38b9470de80ba
Autor:
Yang-Lang Chang, Tan-Hsu Tan, Tsung-Hau Chen, Joon Huang Chuah, Lena Chang, Meng-Che Wu, Narendra Babu Tatini, Shang-Chih Ma, Mohammad Alkhaleefah
Publikováno v:
Remote Sensing, Vol 14, Iss 8, p 1929 (2022)
Agriculture is an important regional economic industry in Asian regions. Ensuring food security and stabilizing the food supply are a priority. In response to the frequent occurrence of natural disasters caused by global warming in recent years, the
Externí odkaz:
https://doaj.org/article/1de3f8eaae484f1eacbb038f2c4ee76c
Autor:
Yang-Lang Chang, Tan-Hsu Tan, Wei-Hong Lee, Lena Chang, Ying-Nong Chen, Kuo-Chin Fan, Mohammad Alkhaleefah
Publikováno v:
Remote Sensing, Vol 14, Iss 7, p 1571 (2022)
The performance of hyperspectral image (HSI) classification is highly dependent on spatial and spectral information, and is heavily affected by factors such as data redundancy and insufficient spatial resolution. To overcome these challenges, many co
Externí odkaz:
https://doaj.org/article/c9745743a99242c2aa4158b3d829fa91
Autor:
Shang-Chih Ma, Mohammad Alkhaleefah, Yang-Lang Chang, Joon Huang Chuah, Wen-Yen Chang, Chiung-Shen Ku, Meng-Che Wu, Lena Chang
Publikováno v:
Sensors, Vol 22, Iss 7, p 2695 (2022)
The Internet of Things (IoT) technology has revolutionized the healthcare industry by enabling a new paradigm for healthcare delivery. This paradigm is known as the Internet of Medical Things (IoMT). IoMT devices are typically connected via a wide ra
Externí odkaz:
https://doaj.org/article/91a39988c70f4b69b55b74642aa0497f
Autor:
Chia-Cheng Yeh, Yang-Lang Chang, Mohammad Alkhaleefah, Pai-Hui Hsu, Weiyong Eng, Voon-Chet Koo, Bormin Huang, Lena Chang
Publikováno v:
Remote Sensing, Vol 13, Iss 1, p 127 (2021)
Due to the large data volume, the UAV image stitching and matching suffers from high computational cost. The traditional feature extraction algorithms—such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Oriented
Externí odkaz:
https://doaj.org/article/6477b4f6972b477fa8b3e05c4e7d67a2
Publikováno v:
Remote Sensing, Vol 13, Iss 1, p 103 (2020)
This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established.
Externí odkaz:
https://doaj.org/article/38d60806bb3446ffa111ddfaeb4310e1