Universidade Federal do Rio Grande do Sul/UFRGS, Departamento de Geodésia, Av. Bento Gonçalves, 9500, 91509-900 Porto Alegre, RS, Brazil.
Universidade Federal do Rio Grande/FURG, Instituto de Oceanografia, Av. Itália, s/n, Km 8, 96201-900 Rio Grande, RS, Brazil.
An Acad Bras Cienc. 2022 Mar 11;94(suppl 1):e20210217. doi: 10.1590/0001-3765202220210217. eCollection 2022.
The classification of Synthetic Aperture Radar (SAR) images by knowledge-based algorithms with elevation and backscatter thresholds were used in several studies to detect the Wet Snow Radar Zone (WSZ) in the Antarctic Peninsula. To identify it more accurately based on its seasonal variations, this study proposed the additional use of a threshold in synthetic images, created by rationing summer and winter sigma linear images. In our algorithm we used the following thresholds to detect the WSZ in Envisat ASAR imageries, using the Radarsat Antarctic Map Digital Elevation Model as ancillary data: i) -25 dB < s0 < -14 dB; ii) slinear summer / slinear winter < 0.4; iii) elevation H < 1,200 m for northern tip and H < 800 m for southern tip of the Antarctic Peninsula. The classified images were post-processed by a focal majority 5 x 5 filter and superimposed by an image of rock outcrops derived from the Antarctic Digital Database. The ratio image threshold allowed discriminating the WSZ from the Dry Snow Radar Zone and radar shadows, as well as transitional areas between this glacier zone and the Frozen Percolation Radar Zone, which would be classified incorrectly if we used only elevation and backscatter thresholds.
基于知识的算法对 SAR 图像进行分类，结合高程和后向散射阈值，已被多项研究用于探测南极半岛的湿雪雷达区 (WSZ)。为了更准确地识别它，本研究提出了在合成图像中使用额外阈值的方法，即用夏季和冬季的 sigma 线性图像分配创建阈值。在我们的算法中，我们使用以下阈值来检测 Envisat ASAR 图像中的 WSZ，使用 Radarsat 南极地图数字高程模型作为辅助数据：i）-25 dB < s0 < -14 dB；ii）slinear summer / slinear winter < 0.4；iii）南极半岛北端的高程 H < 1200 米，南端的高程 H < 800 米。分类后的图像经过 5 x 5 滤波器的聚焦多数滤波器处理，并叠加了南极数字数据库中提取的岩石露头图像。比值图像阈值允许区分 WSZ 与干雪雷达区和雷达阴影，以及该冰川区与冻结渗流雷达区之间的过渡区，如果我们只使用高程和后向散射阈值，这些区域将被错误分类。
