Water Bathymetry(水深测量)研究综述
Water Bathymetry 水深测量 - Optical satellite remote sensing (RS) is a time- and cost-effective approach for shallow-water bathymetry over large areas. [1] Empirical methods for estimating shallow-water bathymetry using passive multispectral satellite imagery are robust and globally applicable, in theory, but they require copious local measurements of water depth for algorithm calibration. [2] On the basis of seniors' research, this paper focuses on Scarborough Island of South China Sea and sets up model of water bathymetry based on Support Vector Machine(SVM). [3] Shallow-water bathymetry based on multispectral satellite imagery (MSI) is an important technology for depth measurement, but it is difficult to obtain a bathymetric map with high reliability and no missing data because of the ubiquitous image noise. [4]光学卫星遥感 (RS) 是一种用于大面积浅水测深的时间和成本效益高的方法。 [1] 使用被动多光谱卫星图像估计浅水测深的经验方法在理论上是稳健且全球适用的，但它们需要大量的水深局部测量以进行算法校准。 [2] 本文在前人研究的基础上，以南海斯卡伯勒岛为研究对象，建立了基于支持向量机（SVM）的水深测量模型。 [3] 基于多光谱卫星影像（MSI）的浅水测深是一项重要的深度测量技术，但由于图像噪声无处不在，难以获得可靠且无缺失数据的测深图。 [4]
Shallow Water Bathymetry 浅水测深
Previous studies of shallow water bathymetry of riverbeds and lakes, experimental studies above sea ice and increasing availability of high-resolution aerial sea ice imagery motivated us to investigate the possibilities to derive pond bathymetry from photogrammetric multi-view reconstruction of the summery ice surface topography. [1] Empirical models have been widely used to retrieve shallow water bathymetry from multispectral/hyperspectral satellite imagery. [2] Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. [3] Shallow water bathymetry is highly significant to regional and national economic development. [4] With the help of different Open Geospatial Consortium protocols, the WebGIS displays different layers of information for 134 PBs including orthophotos, sedimentological/geomorphological beach characteristics, shoreline evolution, geometric and morphological parameters, shallow water bathymetry, and photographs of pocket beaches. [5] To derive shallow water bathymetry for coastal areas, a common approach is to deploy a scanning airborne bathymetric light detection and ranging (LiDAR) system or a shipborne echosounder for ground surveys. [6] A nonlinear machine learning technique was used to derive shallow water bathymetry by combining single beam echosounding measurements and the reflectance of red, green, blue, and near infrared bands of remotely sensed imagery. [7] Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. [8] Shallow water bathymetry is essential information for coastal science and nautical navigation. [9] This study addresses the mapping of shallow water bathymetry with high spatial resolution and accuracy by comparing three remote sensing (RS) approaches: one based on echo sounding (active RS) and two on photogrammetry (passive RS): bathymetric Structure from Motion (SfM) and optical modelling. [10] This paper presents a spatially distributed support vector machine (SVM) system for estimating shallow water bathymetry from optical satellite images. [11] These signature are very important for inland water quality and shallow water bathymetry application. [12] Traditional in situ measurements with topographic equipment provide spatially sparse datasets and they could not map in detail the spatial variability in wave and current fields, the shallow water bathymetry and the beach morphology. [13] The objective of this paper is to explore the feasibility of shallow water bathymetry using satellite two-media photogrammetry. [14] In coastal areas, the concentrations and the optical properties of the water components have a large spatial and temporal variability, due to river discharges and meteo-marine conditions, such as wind, wave and current, and their interaction with shallow water bathymetry. [15] Recently, remote sensing is commonly used to map the shallow water bathymetry since it is frequently captured. [16]以前对河床和湖泊的浅水测深研究、海冰上方的实验研究以及高分辨率航拍海冰图像的可用性增加促使我们研究从夏季冰面地形的摄影测量多视图重建中推导出池塘测深的可能性。 [1] 经验模型已被广泛用于从多光谱/高光谱卫星图像中检索浅水测深。 [2] 全球浅水测深地图为科学研究、环境保护和海洋运输等活动提供了重要信息。 [3] 浅水测深对区域和国家经济发展具有重要意义。 [4] 在不同的开放地理空间联盟协议的帮助下，WebGIS 显示了 134 个 PB 的不同层次的信息，包括正射照片、沉积学/地貌海滩特征、海岸线演变、几何和形态参数、浅水测深和袖珍海滩的照片。 [5] 为了获得沿海地区的浅水测深，一种常见的方法是部署扫描机载测深光探测和测距 (LiDAR) 系统或用于地面测量的船载回声测深仪。 [6] 非线性机器学习技术被用于通过结合单波束回声测量和遥感图像的红、绿、蓝和近红外波段的反射率来推导浅水测深。 [7] 浅水测深对于航海航行避免搁浅以及科学模拟沿海地区的涨潮和大浪具有重要意义。 [8] 浅水测深是海岸科学和航海导航的重要信息。 [9] 本研究通过比较三种遥感 (RS) 方法来解决具有高空间分辨率和精度的浅水测深制图问题：一种基于回声探测（主动 RS）和两种基于摄影测量（被动 RS）：运动测深结构 (SfM)和光学建模。 [10] 本文提出了一种空间分布式支持向量机 (SVM) 系统，用于从光学卫星图像估计浅水测深。 [11] 这些特征对于内陆水质和浅水测深应用非常重要。 [12] 使用地形设备进行的传统原位测量提供了空间稀疏的数据集，无法详细绘制波浪和水流场的空间变化、浅水测深和海滩形态。 [13] 本文的目的是探讨利用卫星二介质摄影测量进行浅水测深的可行性。 [14] 在沿海地区，由于河流流量和海洋气象条件（如风、波浪和水流）及其与浅水测深的相互作用，水成分的浓度和光学特性具有很大的时空变异性。 [15] 最近，由于经常被捕获，因此通常使用遥感来绘制浅水测深图。 [16]