• Àüü
  • ÀüÀÚ/Àü±â
  • Åë½Å
  • ÄÄÇ»ÅÍ
´Ý±â

»çÀÌÆ®¸Ê

Loading..

Please wait....

¿µ¹® ³í¹®Áö

Ȩ Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö > JICCE (Çѱ¹Á¤º¸Åë½ÅÇÐȸ)

JICCE (Çѱ¹Á¤º¸Åë½ÅÇÐȸ)

Current Result Document : 6 / 6 ÀÌÀü°Ç ÀÌÀü°Ç

ÇѱÛÁ¦¸ñ(Korean Title) A Deep Learning Approach for Classification of Cloud Image Patches on Small Datasets
¿µ¹®Á¦¸ñ(English Title) A Deep Learning Approach for Classification of Cloud Image Patches on Small Datasets
ÀúÀÚ(Author) Van Hiep Phung   Eun Joo Rhee  
¿ø¹®¼ö·Ïó(Citation) VOL 16 NO. 03 PP. 0173 ~ 0178 (2018. 09)
Çѱ۳»¿ë
(Korean Abstract)
¿µ¹®³»¿ë
(English Abstract)
Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.
Å°¿öµå(Keyword) Cloud classification   CNN   Data augmentation   SWIMCAT dataset  
ÆÄÀÏ÷ºÎ PDF ´Ù¿î·Îµå