Multiple Deep learning Methods for Small and Blurry Volcanic Ash Cloud Detection for Satellite Imagery

Hidetomo Sakaino, Nutnaree Kleawsirikul, Patamawadee Leepaisomboon, Natnapat Gaviphatt

Abstract: This paper proposes a small and blurry foreground object detection method from highly similar textured background images based on Deep Learning (DL). In satellite imagery, volcanic smoke can be observed as ash clouds that present highly similar shape and texture to surrounding normal clouds. Moreover, very small size of such ash clouds can further impede the detectability when applying previous image processing-based methods. Therefore, a special care for discriminating foreground and background images has been desired. For this issue, this paper presents a multiple, i.e., three-step, DL based important object detection method for satellite imagery, i.e., specially mixed infrared channels of the Himawari-8, with volcanic smoke/ash clouds. Moreover, DL, i.e. Fully Convolutional Network, generalization performances with blurry object contours have been enhanced by multiple loss functions and re-selection of pooling layer. Experimental results on world-wide volcanos have demonstrated that the proposed multiple DL models have outperformed a single DL based detection methods, i.e., edge and contour detection.