Abstract
Abstract. This paper discusses the automatic generation of thermal infrared ortho image mosaics and the extraction of solar cells from these ortho image mosaics. Image sequences are recorded by a thermal infrared (TIR) camera mounted on a remotely piloted aerial system (RPAS). The image block is relatively oriented doing a bundle block adjustment and transferred to a local coordinate system using ground control points. The resulting ortho image mosaic is searched for solar cells. A library of templates of solar cells from thermal images is used to learn an implicit shape model. The extraction of the single solar cells is done by estimating corners and centre points of cells using these shape models in a Markov-Chain-Monte-Carlo algorithm by combining four corners and a centre point. As for the limited geometric resolution and radiometric contrast, most of the cells are not directly detected. An iterative process based on the knowledge of the regular grid structure of a solar cell installation is used to predict further cells and verify their existence by repeating the corner extraction and grammar combination. Results show that this work flow is able to detect most of the solar cells under the condition that the cells have a more or less common radiometric behaviour and no reflections i.e. from the sun occur. The cells need a rectangular shape and have the same orientation so that the model of the grammar is applicable to the solar cells.
Highlights
Today, automatic 3D reconstruction and texture extraction is focused on high resolution images and image sequences from the visual spectrum
The resulting camera orientations and 3D points have to be transferred from the model coordinate system to the global coordinate system either using external GPS/INS orientation information, a matching with given 3D building models, or a coregistration based on ground control points
This papers is focussed on the automatic extraction of solar cells in solar parks from thermal infrared (TIR) images acquired by a remotely piloted aerial system (RPAS)
Summary
Automatic 3D reconstruction and texture extraction is focused on high resolution images and image sequences from the visual spectrum These methods are mainly using homologous points to link the images in a relative orientation and extract 3D coordinates for the homologous points (Hartley and Zisserman, 2004). Whereas in (Reznik and Mayer, 2008) RGB images are used for a Markov-Chain-Monte-Carlo approach for window extraction, first attempts for window extraction from thermal infrared façade textures have been done using grammars like Gestalt system (Michaelsen et al, 2012) This papers is focussed on the automatic extraction of solar cells in solar parks from thermal infrared (TIR) images acquired by a remotely piloted aerial system (RPAS). A transfer to geo-referenced ortho images would allow the localisation of solar cells and the analysis of possible defects using automatic image processing techniques
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