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14 December 2015 Field crop extraction robust to illumination variations based on specularity learning
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Proceedings Volume 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 98151U (2015) https://doi.org/10.1117/12.2205851
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
Abstract
In this paper, we proposed an illumination-invariant crop extraction method based on specularity learning. Several useful contextual cues including object appearance and location inspired by recognition mechanism of human beings were introduced and integrated to machine learning architecture, generating a well-trained highlight region classifier. Combing with the Hue-intensity Look-up table and super-pixel techniques, the classifier gives the final extraction result. Comparing experiment confirmed the validity and feasibility of our method.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhenghong Yu, Cuina Li, and Huabing Zhou "Field crop extraction robust to illumination variations based on specularity learning", Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 98151U (14 December 2015); https://doi.org/10.1117/12.2205851
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