Abstract
Thermal imagery is widely used in various fields of remote sensing. In this study, a novel processing scheme is developed to process the data acquired by the oblique airborne photogrammetric system AOS-Tx8 consisting of four thermal cameras and four RGB cameras with the goal of large-scale area thermal attribute mapping. In order to merge 3D RGB data and 3D thermal data, registration is conducted in four steps: First, thermal and RGB point clouds are generated independently by applying structure from motion (SfM) photogrammetry to both the thermal and RGB imagery. Next, a coarse point cloud registration is performed by the support of georeferencing data (global positioning system, GPS). Subsequently, a fine point cloud registration is conducted by octree-based iterative closest point (ICP). Finally, three different texture mapping strategies are compared. Experimental results showed that the global image pose refinement outperforms the other two strategies at registration accuracy between thermal imagery and RGB point cloud. Potential building thermal leakages in large areas can be fast detected in the generated texture mapping results. Furthermore, a combination of the proposed workflow and the oblique airborne system allows for a detailed thermal analysis of building roofs and facades.
Highlights
Driven by the fast development of uncooled microbolometer detector arrays, uncooled thermal cameras are widely used in applications such as building insulation inspection [1,2], forest fire protection [3], power equipment monitoring [4], flow surface velocimetry [5], gender recognition [6], night vision [7], precision agriculture [8], soil moisture deficit detection [9], and groundwater discharge evaluation [10].Considering that 2D thermal cameras have limited spatial resolution and a narrow field of view (FOV), each image usually only covers a small part of the scene
In the texture mapping procedure, 773, 327, and 712 thermal images were involved for building area, river ecosystem, and soil area, respectively
High-resolution RGB imagery is used for 3D reference model generation, while thermal cameras provide thermal attributes for texture mapping
Summary
Driven by the fast development of uncooled microbolometer detector arrays, uncooled thermal cameras are widely used in applications such as building insulation inspection [1,2], forest fire protection [3], power equipment monitoring [4], flow surface velocimetry [5], gender recognition [6], night vision [7], precision agriculture [8], soil moisture deficit detection [9], and groundwater discharge evaluation [10].Considering that 2D thermal cameras have limited spatial resolution and a narrow field of view (FOV), each image usually only covers a small part of the scene. Driven by the fast development of uncooled microbolometer detector arrays, uncooled thermal cameras are widely used in applications such as building insulation inspection [1,2], forest fire protection [3], power equipment monitoring [4], flow surface velocimetry [5], gender recognition [6], night vision [7], precision agriculture [8], soil moisture deficit detection [9], and groundwater discharge evaluation [10]. Direct applications of 2D thermal images suffer from the absence of 3D details and the disconnection from real 3D structures. Most of the state-of-the-art research focuses on mapping a large number of thermal images to a 3D model [11]. 3D models (e.g., point clouds) provide high-level and large-scale geometric details for interpretation. Thermal attributes provide an additional feature (i.e., radiant temperature) for 3D models, which is useful for classification and object recognition [12]. The fusion of thermal imagery with existing 3D models is attracting more and more attention in recent years
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