An adaptive machine learning approach to improve automatic iceberg detection from SAR images
An adaptive machine learning approach to improve automatic iceberg detection from SAR images
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DOI：
10.1016/j.isprsjprs.2019.08.015
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发表时间：
2019-10-01
影响因子：
12.7
通讯作者：
Mata, Mauricio M.
中科院分区：
文献类型：
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作者：
Barbat, Mauro M.;Wesche, Christine;Mata, Mauricio M.
冰山分布、扩散和融化模式是南大洋热量和淡水平衡的基本方面；然而，人们对这些特征还没有完全了解。这种缺乏了解的部分原因是，在不同的环境条件下准确识别冰山存在困难。为了加深了解，必须有可靠的冰山探测工具，以获得冰山漂移和解体模式的详细图像，从而获得关于流入南大洋的淡水的进一步信息。在这里，我们提出了一种精确的大规模冰山自动检测方法，该方法使用了一种替代的机器学习结构，适用于高分辨率合成孔径雷达(SAR)图像。我们的方法基于适应性的概念，专注于提高在具有广泛的辐射、纹理、大小和形状变异性的模糊环境中识别冰山的性能。该方法的基本原理是超像素分割、集成学习和增量学习。该方法被应用于一个包含586幅ENVISAT高级合成孔径雷达图像的数据集和Radarsat-1南极测绘项目(RAMP)的镶嵌，该镶嵌覆盖了南极广阔的近海地带。这些图像覆盖了具有不同气象、海洋和采集参数(例如波段、偏振)的所有季节的不同后向散射信号下的场景。我们的方法对区分冰山和隐藏在图像中的模糊对象具有很强的适应性。平均假阳性率为2.3+/-0.4%，漏检率为3.3+/-0.4%。总体而言，共探测到冰山9,512座，冰山大小从0.1公里到4567.82公里(2)，平均分类准确率为97.5+/-0.6%。结果证实，本文提出的方法对南极海域广泛存在的冰山探测是稳健的。
Iceberg distribution, dispersion and melting patterns are fundamental aspects in the balance of heat and freshwater in the Southern Ocean; yet these features are not fully understood. This lack of understanding is, in part, due to the difficulties in accurately identifying icebergs in different environmental conditions. To improve the understanding, reliable iceberg detection tools are necessary to achieve a detailed picture of iceberg drift and disintegration patterns, an thus to gain further information on the freshwater input into the Southern Ocean. Here, we present an accurate automatic large-scale iceberg detection method using an alternative machine learning architecture applied to high resolution Synthetic Aperture Radar (SAR) images. Our method is based on the concept of adaptability and focuses on improving the performance of identifying icebergs in ambiguous environmental contexts with wide radiometric, textural, size and shape variability. The fundamentals of the method are centred on superpixel segmentation, ensemble learning and incremental learning. The method is applied to a dataset containing 586 ENVISAT Advanced SAR images acquired during 2003-2005 (Weddell Sea region) and to the Radarsat-1 Antarctic Mapping Project (RAMP) mosaic, covering the Antarctic wide near coastal zone. These images cover scenes under heterogenous backscattering signatures for all seasons with variable meteorological, oceanographic and acquisition parameters (e.g. band, polarization). Our method is highly adaptable to distinguish icebergs from ambiguous objects hidden in the images. The average false positive rate and miss rate are 2.3 +/- 0.4% and 3.3 +/- 0.4%, respectively. Overall, 9512 icebergs with sizes varying from 0.1 to 4567.82 km(2) are detected with average classification accuracy of 97.5 +/- 0.6%. The results confirm that the method presented here is robust for widespread iceberg detection in the Antarctic seas.