@inproceedings {327,
	title = {Automatic registration of inter-band and inter-sensor images using robust complex wavelet feature representations},
	booktitle = {5th Workshop on Pattern Recognition for Remote Sensing},
	year = {2008},
	month = {12/2008},
	address = {Tampa, Florida, USA},
	abstract = {<p>A robust method for registering inter-band and inter-sensor remote sensing images has been designed and implemented. The proposed method introduces noise-resilient and contrast invariant control point detection and control point matching schemes based on robust complex wavelet feature representations. Furthermore, an iterative refinement scheme is introduced for achieving improved control point pair localization and mapping function estimation between the images being registered. The registration accuracy of the proposed method was demonstrated on the registration of multi-spectral optical and synthetic aperture radar (SAR) images. The proposed method achieves better registration accuracy when compared with the state-of-the-art MSSD and ARRSI registration algorithms.</p>
},
	keywords = {ARRSI, automatic registration, contrast invariant control point detection, control point matching schemes, control point pair localization, estimation theory, Feature Extraction, image matching, image registration, image representation, images registration, inter-band images, inter-sensor images, iterative methods, iterative refinement scheme, mapping function estimation, MSSD, multispectral optical images, registration algorithms, Remote Sensing, remote sensing images, robust complex wavelet feature representations, synthetic aperture radar, synthetic aperture radar images, wavelet transforms},
	doi = {http://dx.doi.org/10.1109/PRRS.2008.4783164},
	author = {A Wong and D A. Clausi and P Fieguth}
}
@article {355,
	title = {ARRSI: Automatic registration of remote-sensing images},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {45},
	year = {2007},
	pages = {1483 - 1493},
	abstract = {<p>This paper presents the Automatic Registration of Remote-Sensing Images (ARRSI); an automatic registration system built to register satellite and aerial remotely sensed images. The system is designed specifically to address the problems associated with the registration of remotely sensed images obtained at different times and/or from different sensors. The ARRSI system is capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear. Global and local contrast issues associated with remotely sensed images are addressed in ARRSI using control-point detection and matching processes based on a phase-congruency model. Intensity-difference issues associated with multimodal registration of remotely sensed images are addressed in ARRSI through the use of features that are invariant to intensity mappings during the control-point matching process. An adaptive control-point matching scheme is employed in ARRSI to reduce the performance issues associated with the registration of large remotely sensed images. Finally, a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus is introduced in ARRSI to improve the accuracy of the transformation model between two remotely sensed images while minimizing computational overhead. The ARRSI system has been tested using various satellite and aerial remotely sensed images and evaluated based on its accuracy and computational performance. The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested</p>
},
	keywords = {ARRSI, Automatic Registration of Remote-Sensing Images, contrast issue, control point detection, image registration, intensity-difference issue, matching process, maximum distance sample consensus, Multimodal registration, performance evaluation, random sample and consensus algorithm, Remote Sensing, squared differences registration techniques},
	issn = {0196-2892},
	doi = {http://dx.doi.org/10.1109/TGRS.2007.892601},
	author = {A Wong and D A. Clausi}
}
