Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes

Małgorzata Jarząbek-Rychard,Hans-Gerd Maas,Dong Lin

doi:10.3390/rs12030543

Małgorzata Jarząbek-Rychard, Hans-Gerd Maas + Show 1 more

Open Access

https://doi.org/10.3390/rs12030543

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Abstract

Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high interest. In this study, we propose an approach for a supervised extraction of façade openings (windows and doors) from photogrammetric 3D point clouds attributed to RGB and thermal infrared (TIR) information. The novelty of the proposed approach is in the combination of thermal information with other available characteristics of data for a classification performed directly in 3D space. Images acquired in visible and thermal infrared spectra serve as input data for the camera pose estimation and the reconstruction of 3D scene geometry. To investigate the relevance of different information types to the classification performance, a Random Forest algorithm is applied to various sets of computed features. The best feature combination is then used as an input for a Conditional Random Field that enables us to incorporate contextual information and consider the interaction between the points. The evaluation executed on a per-point level shows that the fusion of all available information types together with context consideration allows us to extract objects with 90% completeness and 95% correctness. A respective assessment executed on a per-object level shows 97% completeness and 88% accuracy.

Highlights

Energy efficiency in buildings is a multifaceted topic that has gained great attention in the last decade
We examine the utility of the generated 3D point clouds, combining thermal information with supportive color and geometric characteristics
Experiments were conducted applying our procedure to two sets of data—façade 1 and façade 2—which were generated as described in the previous sections and differin the calibration method used to extract their Thermal infrared (TIR) attributes

Summary

Introduction

Energy efficiency in buildings is a multifaceted topic that has gained great attention in the last decade. To execute the legislation resolutions, existing buildings require inspections of their energy distribution. The most widely used technique for the performance of energy studies in built-up areas is infrared thermography. The method detects the infrared energy emitted from an object, converts it to temperature, and displays an intensity color-coded image of temperature distribution. Thermal infrared (TIR) images enable us to visualize different thermal faults such as air infiltrations or moisture areas and to detect damages to the building structure; for example, cracks, delamination, or loose tiling. Depending on the final aim of energy auditing, thermal data can be collected in an indoor environment [1,2] or by outdoor measurements including airborne platforms [3] and close-range techniques [4]. A broad review of various infrared thermography applications for building diagnostics is presented in Kylili et al [5]

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