Inception residual attention network for remote sensing image super-resolution

杨利霞

Inception residual attention network for remote sensingimage super-resolution

How to enhance the spatial resolution for a remote sensing image is
an important issue that we face. Many image super-resolution (SR)
techniques have been proposed for this purpose and deep con
/ L& t# K- _3 t0 F2 L1 fvolutional neural network (CNN) is the most effective approach in
6 Z0 e1 c5 Y" B* ~  Q* N, drecent years. However, we observe that most CNN-based SR meth
ods treat low-frequency areas and high-frequency areas equally,
5 h8 U/ X9 g, m) S$ Bhence hindering the recovery of high-frequency information. In this
6 l) F& U( _$ M0 ~) l$ H: U4 fpaper, we propose a network named inception residual attention
* \% o5 k9 f5 c  qnetwork (IRAN) to address this problem. Specifically, we propose
a spatial attention module to make the network adaptively learn
the importance of different spatial areas, so as to pay more atten
tion to the areas with high-frequency information. Furthermore, we
present an inception module to fuse local multilevel features, so as
to provide richer information for reconstructing detailed textures. In
order to evaluate the effectiveness of the proposed method, a large
; N* a( I& r/ s2 enumber of experiments are performed on UCMerced-LandUse data
set and the results show that the proposed method is superior to
the current state-of-the-art methods in both visual effects and
" k8 d$ q- ]) _+ robjective indicators.

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