Shape-constrained segmentation approach for Arctic multiyear sea ice floe analysis

• Main Authors: Tarabalka, Yuliya, Brucker, Ludovic, Ivanoff, Alvaro, Tilton, James
• Other Authors: Models of spatio-temporal structure for high-resolution image processing (AYIN), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), NASA Goddard Space Flight Center (GSFC), IEEE
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2012
• Subjects: [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; Arctic; Sea ice
• Online Access: https://hal.inria.fr/hal-00729033; https://hal.inria.fr/hal-00729033/document; https://hal.inria.fr/hal-00729033/file/2012_IGARSS_Tarabalka_Ice.pdf

International audience The melting of sea ice is correlated to increases in sea surface temperature and associated climatic changes. Therefore, it is important to investigate how rapidly sea ice floes melt. For this purpose, a new TempoSeg method for multitemporal segmentation of multiyear ice floes is proposed. The microwave radiometer is used to track the position of an ice floe. Then, a time series of MODIS images are created with the ice floe in the image center. A TempoSeg method is performed to segment these images into two regions: Floe and Background. First, morphological feature extraction is applied. Then, the central image pixel is marked as Floe, and shape-constrained best merge region growing is performed. The resulting two-region map is post-filtered by applying morphological operators. We have successfully tested our method on a set of MODIS images and estimated the area of a sea ice floe as a function of time.