Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Kefeng JI"'
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Change detection (CD) is essential in remote sensing (RS) for natural resource monitoring, territorial planning, and disaster assessment. With the abundance of data collected by satellite, aircraft, and unmanned aerial vehicles, the utilization of mu
Externí odkaz:
https://doaj.org/article/0149efd337b7453eb9025f2ed6a9c180
Publikováno v:
Leida xuebao, Vol 13, Iss 2, Pp 307-330 (2024)
Synthetic Aperture Radar (SAR), with its coherent imaging mechanism, has the unique advantage of all-day and all-weather imaging. As a typical and important topic, aircraft detection and recognition have been widely studied in the field of SAR image
Externí odkaz:
https://doaj.org/article/921dd5f854a4403fa308241c68ddfd2c
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18834-18851 (2024)
Conventional ship detection using synthetic aperture radar (SAR) is typically limited to fully-focused SAR images, limiting the development of real-time SAR ship detection. Ship detection in the SAR range-compressed domain holds significant real-time
Externí odkaz:
https://doaj.org/article/5385edf3cb324f9b85e7c2c09edd7ac6
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15845-15861 (2024)
Synthetic aperture radar automatic target recognition (SAR ATR) has ushered in a new era dominated by deep-learning (DL) techniques. However, the DL-based recognition systems inevitably confront catastrophic forgetting for learned knowledge and overf
Externí odkaz:
https://doaj.org/article/5cd75c424a3148bb8edf41ccfd758c5b
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4611-4625 (2024)
Deep learning has offered new ideas in SAR ship target recognition. Although many methods improve the recognition performance through the improvement of loss function and migration of deep networks, scattering features as the important intrinsic feat
Externí odkaz:
https://doaj.org/article/d8c7dbb407824960a7a453e2db04ec94
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3284 (2024)
The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR
Externí odkaz:
https://doaj.org/article/7c92bd2e64164eaab80f34940e0b3dd5
Leveraging Visual Language Model and Generative Diffusion Model for Zero-Shot SAR Target Recognition
Publikováno v:
Remote Sensing, Vol 16, Iss 16, p 2927 (2024)
Simulated data play an important role in SAR target recognition, particularly under zero-shot learning (ZSL) conditions caused by the lack of training samples. The traditional SAR simulation method is based on manually constructing target 3D models f
Externí odkaz:
https://doaj.org/article/9d8e353ecb3246d7a36f9be36572d0e4
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 128, Iss , Pp 103707- (2024)
Lack of labeled data is a common problem among synthetic aperture radar (SAR) target recognition, which can be defined as few-shot and limited-data SAR target recognition. The low accuracy under scarce labeled data is mainly due to the sensitivity of
Externí odkaz:
https://doaj.org/article/ba3f0d923ac840689c4e305c4cc821c7
Publikováno v:
Leida xuebao, Vol 11, Iss 3, Pp 347-362 (2022)
Wide-swath Synthetic Aperture Radar (SAR), represented by TopSAR and ScanSAR acquisition modes, can observe a vast area of ocean scenes. However, achieving wide-swath reduces the quality of imaging resolution, which causes the ships captured in wide-
Externí odkaz:
https://doaj.org/article/def18025c04642a29020a3b277cfa650
Publikováno v:
Remote Sensing, Vol 15, Iss 19, p 4660 (2023)
As a safety-related application, visual systems based on deep neural networks (DNNs) in modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when performing real-time inference. Recently, deep ensembles with various defensive strateg
Externí odkaz:
https://doaj.org/article/74cecec02a27439b9cc78c84390bfe60