Optical and SAR image fusion method with coupling gain injection and guided filtering

Fu, Yukai; Yang, Shuwen; Yan, Heng; Xue, Qiaochu; Zhuang, Shi‐Mei; Hu, Xianluo

doi:10.1117/1.jrs.16.046505

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…smooth variable structure filter has also been extensively reviewed for its potential in nonlinear control and estimation in robotics [40,[43][44][45][46][47][48][49][50]. Additionally, the use of Kalman filter and its variants in robotic manipulators has shown promising results [51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69].…”

Section: Motivationmentioning

confidence: 99%

Applications of autonomous rover for radiometric surveillance

Faroukh

Al-Shabi

Khuwaileh

2023

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIV

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The concept of an autonomous rover system to perform maintenance, investigations, and data collection in remote or inaccessible locations has seen an increased demand recently. In this work, an autonomous rover is developed to detect radioactive contamination. The rover utilizes a gas tube radiation detector as an active sensing element and onboard modules to command and control the rover, such as a GNSS receiver, Autopilot controller, and a microcontroller as an onboard controller a communication module. The rover could be controlled by a human operator or autonomous control. In both cases, the operator would be far away from the scene. The rover has many potentially valuable applications, such as radiometric survey and mapping, locating survivors, or aiding in recovering victims after a CBRN disaster. This paper discusses the concept of operations and the design of the autonomous rover.

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Section: Motivationmentioning

confidence: 99%

Applications of autonomous rover for radiometric surveillance

Faroukh

Al-Shabi

Khuwaileh

2023

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIV

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“…The widespread application of multi-source data fusion methods has become common practice [ 20 , 21 ]. These fusion methods aim to capitalize on the complementary strengths of different sensor modalities, enhancing the overall information content and improving the robustness of remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

A Triplet Network Fusing Optical and SAR Images for Colored Steel Building Extraction

Zhang,

Yang,

Yang

et al. 2023

Sensors

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The identification of colored steel buildings in images is crucial for managing the construction sector, environmental protection, and sustainable urban development. Current deep learning methods for optical remote sensing images often encounter challenges such as confusion between the roof color or shape of regular buildings and colored steel structures. Additionally, common semantic segmentation networks exhibit poor generalization and inadequate boundary regularization when extracting colored steel buildings. To overcome these limitations, we utilized the metal detection and differentiation capabilities inherent in synthetic aperture radar (SAR) data to develop a network that integrates optical and SAR data. This network, employing a triple-input structure, effectively captures the unique features of colored steel buildings. We designed a multimodal hybrid attention module in the network that discerns the varying importance of each data source depending on the context. Additionally, a boundary refinement (BR) module was introduced to extract the boundaries of the colored steel buildings in a more regular manner, and a deep supervision strategy was implemented to improve the performance of the network in the colored steel building extraction task. A BR module and deep supervision strategy were also implemented to sharpen the extraction of building boundaries, thereby enhancing the network’s accuracy and adaptability. The results indicate that, compared to mainstream semantic segmentation, this method effectively enhances the precision of colored steel building detection, achieving an accuracy rate of 83.19%. This improvement marks a significant advancement in monitoring illegal constructions and supporting the sustainable development of the Beijing–Tianjin–Hebei metropolitan region.

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