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Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 833-846 (2024)
Full-resolution quality evaluation model for pansharpened images is significant for remote sensing applications, yet presents a challenge of the absence of reference compared with the reduced-resolution approach. To predict the image quality accurate
Externí odkaz:
https://doaj.org/article/a23e659482c04346ad14f146de22d32b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 983-998 (2024)
Object detection under hazy weather conditions is a huge challenge due to the serious obscuration by fog. Recently, to improve detection accuracy in inclement weather, some excellent specifically designed algorithms for foggy conditions have been pre
Externí odkaz:
https://doaj.org/article/42041921ca3e489e9f3eb8367aa428a6
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 1046-1059 (2024)
Synthetic aperture radar (SAR) imagery is indispensable for acquiring a comprehensive, large-scale topographical perspective of the Earth's surface, facilitating the evaluation of diverse scenarios spanning various events. However, integrating SAR im
Externí odkaz:
https://doaj.org/article/2586ed6f2abd4a18b4365c0f6563f412
Autor:
Yuhao Wen, Zhimin Zhang, Xiangrui Meng, Zongsen Lv, Zhen Chen, Yifei Liu, Huaitao Fan, Lei Zhang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 969-982 (2024)
High-resolution, wide-range imaging is crucial for contemporary and future remote sensing surveys. Multichannel synthetic aperture radar (SAR) is an effective tool to fulfill this requirement. Among various multichannel technologies, null steering di
Externí odkaz:
https://doaj.org/article/be0f3851c7354b088f89da2d1db09504
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 908-925 (2024)
The accurate detection of small object in optical remote sensing images (ORSIs) presents a significant challenge due to the background interference and the small size of the objects, making them susceptible to misdetection and omission. In this artic
Externí odkaz:
https://doaj.org/article/50457042135246cb920121ff20301762
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11823-11838 (2024)
Change detection is an essential fundamental task in remote sensing image analysis. Owing to powerful deep abstract feature extraction ability, many deep learning-based change detection methods have emerged recently. Although previous works have reco
Externí odkaz:
https://doaj.org/article/ec35f9627447435bbe47ce85beeed89f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 847-864 (2024)
This article proposes a new method for estimating target polarimetric entropy from dual-polarimetric (DP) synthetic aperture radar (SAR) data based on the reciprocity assumption commonly applied in the processing of full-polarimetric (FP) SAR data. B
Externí odkaz:
https://doaj.org/article/30c40eb40d224acd8330ac03c308685d
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 893-907 (2024)
Recent computer vision research has mainly focused on designing efficient network architectures, with limited exploration of high- and low-frequency information in the high-order frequency domain. This study introduces a novel approach utilizing spat
Externí odkaz:
https://doaj.org/article/e8d60baa83414735a625ee369eb56784
Autor:
Dingyi Zhou, Zhifang Zhao
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 954-968 (2024)
Interferometry synthetic aperture radar (InSAR) is essential for monitoring surface deformation and geological hazards. InSAR applications in complex mountainous regions are challenging due to high noise levels and large phase gradient variations, wh
Externí odkaz:
https://doaj.org/article/24a8ea7553dc4d899890dfb9b1f86643
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 999-1015 (2024)
In recent years, convolutional neural network (CNN) based methods have been widely used in remote sensing image compression tasks. However, CNN is commonly used to extract local information and does not fully utilize global contextual information. Th
Externí odkaz:
https://doaj.org/article/bd4de1d3f5454f538790248f4199ab8b