Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network
AbstractRecently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities. View Full-Text
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Lu, T.; Wang, J.; Zhang, Y.; Wang, Z.; Jiang, J. Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network. Remote Sens. 2019, 11, 1588.
Lu T, Wang J, Zhang Y, Wang Z, Jiang J. Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network. Remote Sensing. 2019; 11(13):1588.Chicago/Turabian Style
Lu, Tao; Wang, Jiaming; Zhang, Yanduo; Wang, Zhongyuan; Jiang, Junjun. 2019. "Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network." Remote Sens. 11, no. 13: 1588.
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