In this study, we evaluated the use of synthetic aperture radar (SAR) and multispectral data to detect aquaculture waterbodies in Southern Bangladesh to quantify fish production on a national scale. For this purpose, we developed an object-based framework comprised of three sequential stages: 1) water detection, 2) feature segmentation, and 3) feature classification. Techniques such as Edge-Otsu for binary thresholding, edge detection with convolution filters, and various supervised and unsupervised machine learning methods were used as part of a workflow. We found that ensemble products combining individual subproducts resulted in higher overall accuracy for water detection (overall detection rate around 60%) and waterbodies classification (overall accuracies up to 79%). Moreover, we showed that SAR data and shape indices played important roles in better-discriminating waterbodies. However, limitations in edge detection outcomes affected the identification of small and isolated aquaculture waterbodies, especially those integrated into rice fields, or in areas with trees.

在这项研究中，我们评估了使用合成孔径雷达 (SAR) 和多光谱数据来检测孟加拉国南部的水产养殖水体，以量化全国范围内的鱼类产量。为此，我们开发了一个基于对象的框架，由三个连续阶段组成：1）水检测，2）特征分割，3）特征分类。诸如用于二进制阈值处理的 Edge-Otsu、使用卷积滤波器进行边缘检测以及各种监督和非监督机器学习方法等技术被用作工作流程的一部分。我们发现，结合单个子产品的集成产品可以提高水检测的整体准确度（整体检测率约为 60%）和水体分类（整体准确度高达 79%）。而且，我们表明 SAR 数据和形状指数在更好地区分水体方面发挥了重要作用。然而，边缘检测结果的局限性影响了小型和孤立水产养殖水体的识别，特别是那些融入稻田或有树木的地区的水体。