Abstract
With the development of remote sensing technology in recent decades, spaceborne sensors with sub-meter and meter spatial resolution (Worldview and PlanetScope) have achieved a considerable image quality to generate 3D geospatial data via a stereo matching pipeline. These achievements have significantly increased the data accessibility in 3D, necessitating adapting these 3D geospatial data to analyze human and natural environments. This dissertation explores several novel approaches based on stereo and multi-view satellite image-derived 3D geospatial data, to deal with remote sensing application issues for built-up area modeling and natural environment monitoring, including building model 3D reconstruction, glacier dynamics tracking, and lake algae monitoring. Specifically, the dissertation introduces four parts of novel approaches that deal with the spatial and temporal challenges with satellite-derived 3D data. The first study advances LoD-2 building modeling from satellite-derived Orthophoto and DSMs with a novel approach employing a model-driven workflow that generates building rectangular 3D geometry models. Secondly, we further enhanced our building reconstruction framework for dense urban areas and non-rectangular purposes, we implemented deep learning for unit-level segmentation and introduced a gradient-based circle reconstruction for circular buildings to develop a polygon composition technique for advanced building LoD2 reconstruction. Our third study utilizes high-spatiotemporal resolution PlanetScope satellite imagery for glacier tracking at 3D level in mid-latitude regions. Finally, we proposed a term as "Algal Behavior Function" to refine the quantification of chlorophyll-a concentrations from satellite imagery in water quality monitoring, addressing algae fluctuations and timing discrepancies between satellite observations and field measurements, thus enhancing the precision of underwater algae volume estimates. Overall, this dissertation demonstrates the extensive potential of satellite photogrammetry applications in addressing urban and environmental challenges. It further showcases innovative analytical methodologies that enhance the applicability of adapting stereo and multi-view very high-resolution satellite-derived 3D data. (See full abstract in the document)
Abstract (translated)
在近几十年来，遥感技术的发展使得空间分辨率亚米级和米级的陆地传感器（Worldview和PlanetScope）能够获得相当大的图像质量，通过立体匹配方法生成三维地理数据。这些成就是显著增加了三维数据的可访问性，迫使将这些三维地理数据应用于人类和自然环境的研究。本论文探讨了基于立体和多维卫星图像的几个新的方法，以解决建筑建模和自然环境监测中的遥感应用问题，包括建筑模型3D重建、冰川 dynamics跟踪和湖泊藻类监测。具体来说，论文介绍了一种处理卫星基于3D数据的空间和时间挑战的新方法。第一研究进展了一种利用新方法生成基于卫星Orthophoto和DSM的LOD-2级建模方法。第二，我们进一步提高了建筑重建框架，以应对密度的城市地区和非矩形目的，我们为环形建筑进行了深度学习，并引入了基于圆的曲线重建方法，以发展高级建模LOD2的聚合法。我们的第三研究利用高空间时间分辨率PlanScope卫星图像在 Mid-latitude 地区对冰川进行跟踪。最后，我们提出了一个称为"Algal Behavior Function"的术语，以定量测量水质量监测中卫星图像中蓝藻浓度，解决藻类波动和卫星观测与现场测量之间的时间差异，从而提高水下藻类体积估计的精度。总的来说，本论文展示了卫星摄影测量在城市和环境挑战中广泛应用的潜力。它还展示了创新的数据分析方法，提高了适应立体和多维高分辨率卫星数据的可行性。（ see the full abstract in the document）
URL
https://arxiv.org/abs/2404.12487
