TY  - JOUR
T1  - GAN considering ERF for High-resolution Map Generation
AU  - Lee, Gi Eon 
JO  - Journal of KIISE, JOK
PY  - 2019
DA  - 2019/1/14
DO  - 10.5626/JOK.2019.46.2.122
KW  - semantic segmentation
KW  - effective receptive fields
KW  - generative adversarial network
KW  - patch-wise auto-encoder
KW  - high resolution image transform
KW  - connection imbalance fields
AB  - The paper proposes a network structure for a generative adversarial network (GAN) suitable for high resolution image transformation. For analysis of the resolution classification relation necessary for high resolution image conversion, the effective size of the receptive fields of each encoder is calculated and new connection imbalance fields defined. We can reduce the total number of layers by connecting the encoder and decoder to the patch size, we reduce the total number of layers and the appropriate effective receptive fields and parameter usability confirmed through experiments. To solve the problem of simultaneously providing resolution and classification in high resolution image conversion, a network structure capable of converting high resolution satellite images is suggested experimentally. Additionally, the validity of the network structure that simultaneously improves the resolution and classification is confirmed by comparing and analyzing the receptive fields of the proposed network and the existing network’s receptive fields. The proposed network is then quantitatively verified by comparing the proposed network with the existing network by use of objective numerical value through SSIM, an image similarity analysis method.