Zobrazeno 1 - 10
of 378
pro vyhledávání: '"Multiscale feature fusion"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 176-190 (2025)
Remote sensing (RS) image change detection (CD) aims to identify areas of interest that have changed between bitemporal images. For complex scenarios (e.g., varying lighting conditions), the diverse shapes and scales of the changed areas is especiall
Externí odkaz:
https://doaj.org/article/fc971e66707b4c7eb6b263e0be6f5c5b
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 6, Pp 102113- (2024)
In drone aerial target detection tasks, a high proportion of small targets and complex backgrounds often lead to false positives and missed detections, resulting in low detection accuracy. To improve the accuracy of the detection of small targets, th
Externí odkaz:
https://doaj.org/article/ddbaa92020cf4cc09f7ecc0ef56b6c08
Publikováno v:
Heritage Science, Vol 12, Iss 1, Pp 1-15 (2024)
Abstract To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on
Externí odkaz:
https://doaj.org/article/bd67cb6cdf0947409c1deae1d14a1398
Publikováno v:
IEEE Access, Vol 12, Pp 185740-185756 (2024)
Addressing the current industrial methods for surface defect detection, which suffer from issues such as low detection efficiency, elevated rates of false positives, and inadequate real-time capabilities, this paper proposes an high-precision industr
Externí odkaz:
https://doaj.org/article/86d047a065e54e63808109444826df56
MFIHNet: Multiscale Feature Interaction Hybrid Network for Change Detection of Remote Sensing Images
Autor:
Lin Cao, Qi Liu, Shu Tian, Lihong Kang, Jing Tian, Xiangwei Xing, Kangning Du, Huanyu Bian, Peiran Song, Yanan Guo, Chunzhuo Fan, Chong Fu, Ye Zhang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16672-16691 (2024)
Remote sensing image change detection (RSCD) based on deep learning technology has made remarkable achievements. Meanwhile, the enhancement of network architectures and advancements in optimization algorithms have pushed RSCD performance to a higher
Externí odkaz:
https://doaj.org/article/7dc777dd37de485db5169eee1aa113b2
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16815-16830 (2024)
The rapid increase in spatial resolution of remote sensing scene images (RSIs) has led to a concomitant increase in the complexity of the spatial contextual information contained therein. The coexistence of numerous smaller features makes it challeng
Externí odkaz:
https://doaj.org/article/38d347f955554cf0a50d461183589510
Publikováno v:
IEEE Access, Vol 12, Pp 105055-105068 (2024)
Pavement distress detection is crucial in road health assessment and monitoring. However, there are still some challenges in extracting pavement distress based on deep learning: such as insufficient segmentation, extraction errors and discontinuities
Externí odkaz:
https://doaj.org/article/3e3f580383b64cc4ac9a4fa6346891e1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 6253-6264 (2024)
Extracting buildings from high-resolution remote sensing imagery (HRSI) is of great significance to emergency management, land resource utilization, and analysis, as well as city planning and construction. However, due to the complex backgrounds and
Externí odkaz:
https://doaj.org/article/f1bdbfc06302454bafc3e275e076e720
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9615-9627 (2024)
The task of background suppression in infrared small-target scenarios aims to eliminate irregular noisy backgrounds while preserving targets with high-frequency features. In infrared small-target scenes at long distances, the backgrounds become compl
Externí odkaz:
https://doaj.org/article/365383cdbfe24c2f887610bc3672394b
Publikováno v:
IEEE Access, Vol 12, Pp 45986-46001 (2024)
Rotated object detection in remote sensing images presents a highly challenging task due to the extensive fields of view and complex backgrounds. While Convolutional Neural Networks (CNNs) and Transformer networks have made progress in this area, the
Externí odkaz:
https://doaj.org/article/1964e4c41b044bfaadb25517d9290508