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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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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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Shihao Ma, Jiao Wu, Zhijun Zhang and Yala Tong
Addressing the limitations, including low automation, slow recognition speed, and limited universality, of current mudslide disaster detection techniques in remote sensing imagery, this study employs deep learning methods for enhanced mudslide disaster d...
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Jingxiong Lei, Xuzhi Liu, Haolang Yang, Zeyu Zeng and Jun Feng
High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate back...
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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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