An Integrated Way to Monitor the Health Condition of Building Facades by Analyzing Thermal Infrared Images

Thermography is widely used for defect detection through analysing thermography. Thermography is used to record the surface temperatures with a thermal infrared camera. Visual interpretation can be employed to identify defects according to the differences in surface temperatures. Recently, image-processing techniques have provided a quantitative way to detect defects rather than visual interpretation. Robust principal component analysis (RPCA) is employed to analyze a series of thermal infrared images [1]. Traditionally, principal component analysis (PCA) is used to investigate a series of thermal infrared images by projecting the thermal data onto a low-dimension space such that a template image inherits the significant characteristics of the given thermal data. However, PCA introduces noise effects such that the template generated with PCA contains the noise effects. RPCA offers a similar way to project the given data onto a low-dimension space and a feature space and provides a robust way to depress the noise effects. With the introduction of RPCA, a template image can be selected from the low- dimension space, and image segmentation is employed to segment the template. Those segmented regions illustrated on the segmented template indicate the surface temperature differences, and those differences can be used as clues to identify the defects.