This article proposes the mapping of the concentration of sea ice in the oceanic region of the western Antarctic Peninsula using backscatter coefficients and textural features from synthetic aperture radar (SAR) C-band, horizontal single polarization (HH) images obtained by the Sentinel 1 A satellite sensor. Pure samples of open water and sea ice were obtained from SAR images with the aid of PlanetScope optical images during the austral winter. The statistical technique of logistic regression was applied to identify the best textural features to discriminate the two sampled targets. These selected textural features were used with the backscatter coefficients of SAR images in a maximum likelihood supervised classifier to analyze the images. With sea ice classified in the time series between 2016 and 2019 during winter and austral spring, the concentration of sea ice in the study region was calculated and mapped. The textural features statistically indicated by the logistic regression were gray level co-occurrence matrix (GLCM) Mean and GLCM Variance, resulting in the supervised classification having an accuracy of 86% and precision ranging from 86% to 98% in the validation stage.

本文提出了使用来自 Sentinel 1 A 获得的合成孔径雷达 (SAR) C 波段水平单极化 (HH) 图像的反向散射系数和纹理特征来绘制南极半岛西部海洋区域的海冰浓度图。卫星传感器。在南方冬季期间，借助 PlanetScope 光学图像，从 SAR 图像中获得了开阔水域和海冰的纯样本。应用逻辑回归的统计技术来识别最佳纹理特征以区分两个采样目标。这些选定的纹理特征与最大似然监督分类器中的 SAR 图像的反向散射系数一起用于分析图像。随着海冰在 2016 年至 2019 年冬季和南春的时间序列中分类，计算并绘制了研究区域内海冰的浓度。Logistic回归统计表明的纹理特征是灰度共生矩阵（GLCM）Mean和GLCM Variance，导致监督分类在验证阶段具有86％的准确度和86％至98％的精度。