In:
Annals of Glaciology, International Glaciological Society, Vol. 56, No. 69 ( 2015), p. 285-294
Abstract:
This paper investigates automatic segmentation and classification of C-band, polarimetric synthetic aperture radar (SAR) satellite images of Arctic sea ice under freezing conditions prior to melt. The objective is to investigate the robustness of the results obtained under slightly varying environmental conditions and different viewing geometries. Initially, three geographically overlapping SAR images from consecutive days are incidence-angle corrected and segmented into unknown classes. The segmentation is performed by an unsupervised mixture-of-Gaussian segmentation algorithm utilizing six features extracted from the polarimetric data. After segmentation, the segments are contextually smoothed. One segmented image is manually labelled based on in situ data and expert knowledge. Using this scene as reference, we consider two strategies for class labelling of the other scenes. The first manually labels the classes based on visual inspection of the reference; the second utilizes various statistical distance measures to automatically assign each unknown class to the statistically nearest reference class. These two scenes are also classified pixel-wise by a supervised classification algorithm based on the reference data. Poor classification results are obtained when the incidence angle is very different from the reference scene. Similar viewing geometries reveal good classification and labelling results, the latter regardless of the distance measure used.
Type of Medium:
Online Resource
ISSN:
0260-3055
,
1727-5644
DOI:
10.3189/2015AoG69A802
Language:
English
Publisher:
International Glaciological Society
Publication Date:
2015
detail.hit.zdb_id:
2122400-6
SSG:
14
Permalink
