Seamless change detection and mosaicing for aerial imagery

The color appearance of an object can vary widely as a function of camera sensitivity and ambient illumination. In this paper, we discuss a methodology for seamless interfacing across imaging sensors and under varying illumination conditions for two very relevant problems in aerial imaging, namely, change detection and mosaicing. The proposed approach works by estimating surface reflectance which is an intrinsic property of the scene and is invariant to both camera and illumination. We advocate SIFT-based feature detection and matching in the reflectance domain followed by registration. We demonstrate that mosaicing and change detection when performed in the high-dimensional reflectance space yields better results as compared to operating in the 3-dimensional color space.

[1]  Joost van de Weijer,et al.  Computational Color Constancy: Survey and Experiments , 2011, IEEE Transactions on Image Processing.

[2]  Bui Tuong Phong.  Illumination for computer generated pictures , 1975, Commun. ACM.

[3]  Michael S. Brown,et al.  Training-Based Spectral Reconstruction from a Single RGB Image , 2014, ECCV.

[4]  Huang Yumin,et al.  A PHYSICAL APPROACH TO COLOR IMAGE UNDERSTANDING , 1991 .

[5]  Ahsan Kareem,et al.  Color balancing for change detection in multitemporal images , 2012, 2012 IEEE Workshop on the Applications of Computer Vision (WACV).

[6]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[7]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Junichi Nakamura,et al.  Image Sensors and Signal Processing for Digital Still Cameras , 2005 .

[9]  Takahiro Okabe,et al.  Fast Spectral Reflectance Recovery Using DLP Projector , 2010, ACCV.

[10]  Sabine Süsstrunk,et al.  What is the space of spectral sensitivity functions for digital color cameras? , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[11]  Shahriar Negahdaripour,et al.  Revised Definition of Optical Flow: Integration of Radiometric and Geometric Cues for Dynamic Scene Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  寺西 信一.  Image Sensors and Signal Processing for Digital Still Cameras, Edited by Junichi Nakamura, CRC Press Taylor & Francis Group刊, 2005年8月5日発行, 234×155×23mm, 336頁, 定価($169.95) , 2008 .

[13]  Reiner Lenz,et al.  Evaluation and unification of some methods for estimating reflectance spectra from RGB images. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[14]  Michael S. Brown,et al.  Raw-to-Raw: Mapping between Image Sensor Color Responses , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  John C. Seymour.  Why do color transforms work? , 1997, Electronic Imaging.

[16]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[17]  François Pitié,et al.  Automated colour grading using colour distribution transfer , 2007, Comput. Vis. Image Underst..

[18]  Graham D. Finlayson,et al.  Colour by correlation: a simple, unifying approach to colour constancy , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[19]  Siamak Khorram,et al.  The effects of image misregistration on the accuracy of remotely sensed change detection , 1998, IEEE Trans. Geosci. Remote. Sens..

[20]  Jinwei Gu,et al.  Recovering spectral reflectance under commonly available lighting conditions , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.