Seafloor substrate mapping has become increasingly important to guide the management of marine ecosystems. Full coverage substrate maps, however, cannot easily be created from point samples (e.g. grabs, videos) as a result of the time required for collection and their discrete spatial extent. Instead, relationships between substrate types and surrogate variables as obtained from bathymetric or backscatter data can be modelled to build predictive substrate maps. As calculation of these surrogate variables is scale-dependent, the scale(s) of analysis need(s) to be selected first, with multiple scales likely required to adequately capture substrate characteristics. This paper proposes an objective and automatic self-adaptive analysis scale determination approach at each bathymetric point to extract terrain features (e.g. slope, aspect, etc). Object-based image analysis (OBIA) is also used to calculate additional texture features for segmented backscatter image objects. Random Forest classification is then used to model the relationship between these extracted features and substrate types interpreted from ground-truth video data, and full-coverage seafloor substrate maps are produced. The proposed method was applied on two datasets from Newfoundland, Canada, and demonstrated good performance in terms of both overall (>80%) and per-class accuracies. The proposed method is easily transferable to other study areas and provides an objective, repeatable means for classifying seafloor substrates for environmental protection and management of marine habitats.

中文翻译：

---

海底基质分类中地形特征自适应分析尺度确定

海底基质测绘对于指导海洋生态系统的管理变得越来越重要。然而，由于收集所需的时间及其离散的空间范围，无法轻松地从点样本（例如抓图、视频）创建全覆盖基质图。相反，可以对从测深或反向散射数据获得的基质类型和替代变量之间的关系进行建模，以构建预测基质图。由于这些替代变量的计算与尺度相关，因此需要首先选择分析尺度，可能需要多个尺度才能充分捕获底物特征。本文提出了一种客观、自动的自适应分析尺度确定方法，在每个测深点提取地形特征（如坡度、坡向等）。基于对象的图像分析 (OBIA) 还用于计算分段反向散射图像对象的附加纹理特征。然后使用随机森林分类对这些提取的特征和从地面实况视频数据解释的基质类型之间的关系进行建模，并生成全覆盖的海底基质图。所提出的方法应用于加拿大纽芬兰的两个数据集，并在总体精度（> 80％）和每类精度方面表现出良好的性能。所提出的方法很容易转移到其他研究领域，并为海底基质分类提供客观、可重复的方法，以保护和管理海洋栖息地。