Zobrazeno 1 - 10
of 284
pro vyhledávání: '"optical remote sensing image"'
Publikováno v:
Hangkong bingqi, Vol 31, Iss 3, Pp 94-100 (2024)
Optical remote sensing image classification is one of the key technologies in the field of Earth observation. In recent years, researchers have proposed optical remote sensing image classification using deep neural networks. Aiming at the problem of
Externí odkaz:
https://doaj.org/article/17757c5d400843a4a542779b2dec5e5f
Publikováno v:
IEEE Access, Vol 12, Pp 140809-140822 (2024)
Aiming at the YOLO (You Only Look Once) algorithm’s low detection accuracy caused by the complex background environment and large target scale difference in optical remote sensing image detection, the lightweight convolution fusion attention mechan
Externí odkaz:
https://doaj.org/article/c3cb9830801642858cd9b9e6dd312aad
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13809-13823 (2024)
Salient object detection in optical remote sensing images presents distinct challenges, primarily due to the small scale and background similarity of salient objects in images captured by satellite and aerial sensors. Traditional approaches often fai
Externí odkaz:
https://doaj.org/article/84b46b61318c4c528b68a7bde7bf2b92
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 2133-2148 (2024)
In recent years, change detection (CD) of optical remote sensing images has made remarkable progress through using deep learning. However, current CD deep learning methods are usually improved from the semantic segmentation models, and focus on enhan
Externí odkaz:
https://doaj.org/article/10ac4d09992c4e48b5380843c5fff4ec
Publikováno v:
地质科技通报, Vol 42, Iss 6, Pp 63-75 (2023)
Objective Deep unstable slopes develop in the Minjiang River Basin, and revealing their deformation evolution characteristics is of great significance for stability evaluation and disaster prevention. Methods In this study, taking the large-scale dum
Externí odkaz:
https://doaj.org/article/6d65c617a92a4bb89cc8209267f41920
Publikováno v:
Remote Sensing, Vol 16, Iss 16, p 2953 (2024)
Radiation anomalies in optical remote sensing images frequently occur due to electronic issues within the image sensor or data transmission errors. These radiation anomalies can be categorized into several types, including CCD, StripeNoise, RandomCod
Externí odkaz:
https://doaj.org/article/0e3bb3a9d977449ca7a0f07af9672418
Publikováno v:
Sensors, Vol 24, Iss 8, p 2443 (2024)
The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only pro
Externí odkaz:
https://doaj.org/article/768220a766af4954b1f8b62afc8f99e9
Autor:
Huilan Luo, Bocheng Liang
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 6980-6994 (2023)
Despite salient object detection in natural images has made remarkable progress, it is still an emerging and challenging problem to detect salient objects from optical remote sensing images [remote sensing image salient object detection (RSI-SOD)]. T
Externí odkaz:
https://doaj.org/article/965a039ac70f4428949926fbdc44d2cb
Publikováno v:
Remote Sensing, Vol 16, Iss 4, p 624 (2024)
Salient Object Detection (SOD) is gradually applied in natural scene images. However, due to the apparent differences between optical remote sensing images and natural scene images, directly applying the SOD of natural scene images to optical remote
Externí odkaz:
https://doaj.org/article/08aaaa1d3b864f8ca56f91350625e63d
Publikováno v:
Tongxin xuebao, Vol 43, Pp 190-203 (2022)
Object detection is the core issue in the interpretation of optical remote sensing images, and it is widely used in fields such as intelligence reconnaissance, target monitoring, and disaster rescue.Firstly, combined with the research progress of dee
Externí odkaz:
https://doaj.org/article/739bd6678e7f4c99a38038326ea5d24d