Building recognition based on big template in FLIR images

Jiangwei Zhang; Zhaodong Niu; Songlin Liu; Fang Liu; Zengping Chen

doi:10.1117/12.2066634

23 October 2014 Building recognition based on big template in FLIR images

Jiangwei Zhang, Zhaodong Niu, Songlin Liu, Fang Liu, Zengping Chen

Proceedings Volume 9244, Image and Signal Processing for Remote Sensing XX; 92441T (2014) https://doi.org/10.1117/12.2066634
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

In order to enhance the robustness of building recognition in forward-looking infrared (FLIR) images, an effective method based on big template is proposed. Big template is a set of small templates which contains a great amount of information of surface features. Its information content cannot be matched by any small template and it has advantages in conquering noise interference or incompleteness and avoiding erroneous judgments. Firstly, digital surface model (DSM) was utilized to make big template, distance transformation was operated on the big template, and region of interest (ROI) was extracted by the way of template matching between the big template and contour of real-time image. Secondly, corners were detected from the big template, response function was defined by utilizing gradients and phases of corners and their neighborhoods, a kind of similarity measure was designed based on the response function and overlap ratio, then the template and real-time image were matched accurately. Finally, a large number of image data was used to test the performance of the algorithm, and optimal parameters selection criterion was designed. Test results indicate that the target matching ratio of the algorithm can reach 95%, it has effectively solved the problem of building recognition under the conditions of noise disturbance, incompleteness or the target is not in view.

Citation Download Citation

Jiangwei Zhang, Zhaodong Niu, Songlin Liu, Fang Liu, and Zengping Chen "Building recognition based on big template in FLIR images", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92441T (23 October 2014); https://doi.org/10.1117/12.2066634

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