Laser & Optoelectronics Progress, Volume. 56, Issue 6, 061101(2019)
Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System
Fig. 1. Overall structure of assessment model
Fig. 2. Single-channel assessment framework based on parallel CNN
Fig. 3. VGG16 network structure
Fig. 4. Influences of blur and noise on visual characteristics of remote sensing images. (a) Original image; (b) image with noise; (c) image with noise once more; (d) image with blur; (e) image with blur once more
Fig. 5. Remote sensing images with different texture complexity. (a) S=9.2784; (b) S=23.1248; (c) S=19.0502; (d) S=14.9255; (e) S=20.1074; (f) S=13.2125; (g) S=23.2592; (h) S=18.7957
Fig. 6. Remote sensing images acquired by QuickBird-2 satellite. (a) Harbor; (b) vegetation; (c) road; (d) buildings
Fig. 7. Overall assessment results by proposed method
Fig. 8. Fitting scatter plot between proposed method and SSIM, PSNR, FSIM. (a) SSIM; (b) PSNR; (c) FSIM
Fig. 9. Fitting curves of DMOS for different assessment methods in LIVEMD dataset. (a) SSIM; (b) proposed method; (c) PSNR; (d) FSIM
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Di Liu, Yingchun Li. Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061101
Category: Imaging Systems
Received: Sep. 18, 2018
Accepted: Sep. 30, 2018
Published Online: Jul. 30, 2019
The Author Email: Li Yingchun (13910953181@139.com)