Fully Squeezed Multiscale Inference Network for Fast and Accurate Saliency Detection in Optical Remote-Sensing Images

Recently, salient object detection in optical remote-sensing images (RSIs) has received more and more attention. To tackle the challenges of RSIs including large-scale variation of objects, cluttered background, irregular shape of objects, and big difference in illumination, the cutting-edge convolu...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Hauptverfasser:	Shen, Kunye, Zhou, Xiaofei, Wan, Bin, Shi, Ran, Zhang, Jiyong
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Computational cost Computational efficiency Computational modeling Computer applications Computing costs Convolution Decoding Detection Dimensions Feature maps fully squeeze Inference Kernel Mathematical models Modelling Modules multiscale Neural networks Object recognition Optical imaging Optical network units optical remote-sensing images (RSIs) Optical sensors Parameters Remote sensing Salience saliency detection
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