CSIRO Sentinel-1 SAR image dataset of oil- and non-oil features for machine learning ( Deep Learning )

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips. This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas) - "1" for those containing oil features. This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset. Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors. Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905 Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

这个数据集是什么：这是一个精心整理的二元分类图像数据集，包含灰度（单波段）、400×400像素大小的图像切片，格式为JPEG。这些切片从经过处理的Sentinel-1合成孔径雷达（SAR）卫星场景中提取，覆盖全球多个区域，包含清晰开阔海域切片、类似物（如风或生物成因特征）切片以及浮油切片。该二元数据集的切片标记规则如下：- "0"表示不包含任何油类特征的切片（类似物或清洁海域）；- "1"表示包含油类特征的切片。该二元数据集存在不平衡性，偏向标记为"0"的切片（即无油类特征），其占比为66%。标记为"1"的含油类特征切片占比为34%。

数据集用途：该数据集可用于机器学习（包括深度学习）算法的训练、验证和/或测试，以实现SAR图像中的油类特征检测。它直接适用于欧洲空间局Sentinel-1 SAR任务的算法开发（网址：https://sentinel.esa.int/web/sentinel/missions/sentinel-1），也可能适用于其他SAR卫星传感器的检测算法开发。

数据集概览：切片总数（两类）为N=5,630。分类0、1的数量分别为3,725和1,905，总计5,630。更多信息和描述请参见提供的ReadMe文件（ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt）