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Fadi Shaar, Arif Yilmaz, Ahmet Ercan Topcu and Yehia Ibrahim Alzoubi
Recognizing aircraft automatically by using satellite images has different applications in both the civil and military sectors. However, due to the complexity and variety of the foreground and background of the analyzed images, it remains challenging to ...
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Yuchen Dong, Heng Zhou, Chengyang Li, Junjie Xie, Yongqiang Xie and Zhongbo Li
Camouflaged object detection (COD) is an arduous challenge due to the striking resemblance of camouflaged objects to their surroundings. The abundance of similar background information can significantly impede the efficiency of camouflaged object detecti...
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Jiaming Bian, Ye Liu and Jun Chen
In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network?s performance ...
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Ruoyang Li, Shuping Xiong, Yinchao Che, Lei Shi, Xinming Ma and Lei Xi
Semantic segmentation algorithms leveraging deep convolutional neural networks often encounter challenges due to their extensive parameters, high computational complexity, and slow execution. To address these issues, we introduce a semantic segmentation ...
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Yuto Kamiwaki and Shinji Fukuda
This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. The input variables for the SPAD value prediction using Random Forests, XGBoost, and LightGBM were RGB values, HSL v...
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