Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method can be effective for registering optical multimodal remote sensing images that have been captured with different imaging sensors.

[1]  S. Gedam,et al.  Area Based Image Matching Methods – A Survey , 2012 .

[2]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[3]  B. S. Manjunath,et al.  Registration Techniques for Multisensor Remotely Sensed Imagery , 1996 .

[4]  Arko Lucieer,et al.  Sensor correction and radiometric calibration of a 6-band multispectral imaging sensor for UAV remote sensing , 2012 .

[5]  Laurence C. Smith,et al.  Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic , 2008, IEEE Geoscience and Remote Sensing Letters.

[6]  Laurence C. Smith,et al.  Automated Image Registration Based on Pseudoinvariant Metrics of Dynamic Land-Surface Features , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[8]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[9]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[10]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[11]  Jianglin Ma,et al.  Fully Automatic Subpixel Image Registration of Multiangle CHRIS/Proba Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Chenghai Yang,et al.  Evaluation of an Airborne Remote Sensing Platform Consisting of Two Consumer-Grade Cameras for Crop Identification , 2016, Remote. Sens..

[13]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Andrea Maria Lingua,et al.  An Image-Based Approach for the Co-Registration of Multi-Temporal UAV Image Datasets , 2016, Remote. Sens..

[15]  Ayman Habib,et al.  Line‐based modified iterated Hough transform for automatic registration of multi‐source imagery , 2004 .

[16]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

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

[18]  Birgit Kleinschmit,et al.  Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection , 2014, Remote. Sens..

[19]  Christopher G. Harris,et al.  3D positional integration from image sequences , 1988, Image Vis. Comput..

[20]  Farzin Mokhtarian,et al.  Robust Image Corner Detection Through Curvature Scale Space , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[22]  Jun Chen,et al.  Nonrigid registration of remote sensing images via sparse and dense feature matching. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  Yun Zhang,et al.  Combination of feature-based and area-based image registration technique for high resolution remote sensing image , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[24]  Stefan B. Williams,et al.  Spectral characterization of COTS RGB cameras using a linear variable edge filter , 2013, Electronic Imaging.

[25]  Mila Nikolova,et al.  A Fast Algorithm for Exact Histogram Specification. Simple Extension to Colour Images , 2013, SSVM.

[26]  Barbara G. Grant,et al.  UAV imagery analysis: challenges and opportunities , 2017, Defense + Security.

[27]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[28]  Arko Lucieer,et al.  Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) , 2015, Remote. Sens..

[29]  Mauricio Galo,et al.  Generating Virtual Images from Oblique Frames , 2013, Remote. Sens..

[30]  Mac McKee.  The remote sensing data from your UAV probably isn't scientific, but it should be! , 2017, Commercial + Scientific Sensing and Imaging.

[31]  Andrew Zisserman,et al.  MLESAC: A New Robust Estimator with Application to Estimating Image Geometry , 2000, Comput. Vis. Image Underst..

[32]  Remy Dehaan.  Evaluation of Unmanned Aerial Vehicle (UAV)-Derived imagery for the detection of Wild Radish in Wheat , 2015 .

[33]  Amin Sedaghat,et al.  Remote Sensing Image Matching Based on Adaptive Binning SIFT Descriptor , 2015, IEEE Transactions on Geoscience and Remote Sensing.