Hyperspectral monitoring of large areas (more than 10 km2) can be achieved via the use of a system employing spectrometers and CMOS cameras. A robust and efficient algorithm for automatically combining multiple, overlapping images of a scene to form a single composition (i.e., for the estimation of the point-to-point mapping between views), which uses only the information contained within the images themselves is described here. The algorithm, together with the 2D fast Fourier transform, provides an estimate of the displacement between pairs of images by accounting for rotations and changes of scale. The resulting mosaic was successively georeferenced within the WGS-84 geographic coordinate system. This paper also addresses how this information can be transferred to a push broom type spectral imaging device to build the hyperspectral cube of the area prior to land classification. The performances of the algorithm were evaluated using sample images and image sequences acquired during a proximal sensing field campaign conducted in San Teodoro (Olbia-Tempio—Sardinia). The hyperspectral cube closely corresponds to the mosaic. Mapping allows for the identification of objects within the image and agrees well with ground-truth measurements.
References
[1]
Lillesand, T.; Kiefer, R.W.; Chipman, J.W. Remote Sensing and Image Interpretation, 5th ed. ed.; John Wiley & Sons: New York, NY, USA, 2004.
[2]
Rossini, M.; Panigada, C.; Meroni, M.; Colombo, R. Assessment of oak forest condition based on leaf biochemical variables and chlorophyll fluorescence. Tree Physiol. 2006, 26, 1487–1496.
[3]
Rufino, G; Moccia, A. Integrated VIS-NIR Hyperspectral/Thermal-IR Electro-Optical Payload System for a Mini-UAV; Infotech@Aerospace: Arlington, Virginia, 2005.
Gorsevski, P.V.; Gessler, P.E. The design and development of a hyperspectral and multispectral airborne mapping system. ISPRS J. Photogr. Remote Sens. 2009, 64, 184–192.
[7]
Capel, D. Image Mosaicing and Super-Resolution; Springer-Verlag: London, UK, 2004.
[8]
Cheeseman, P.; Kanefsky, B.; Kraft, R.; Stutz, J. Super-Resolved Surface Reconstruction from Multiple Images. Technical Report FIA-94-12; NASA Ames Research Center: Moffet Field, CA, USA, 1994.
[9]
Vanderwalle, P.; Süsstrunk, S.; Vetterli, M. A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP J. Appl. Signal Process. (Special Issue on Super-Resolution) 2006, 2006, 1–14.
[10]
Goshtasby, A. Piecewise linear mapping functions for image registration. Pattern Recognit. 1986, 19, 459–468.
[11]
Gonzales, R.C.; Woods, R.E. Digital Image Processing; Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, USA, 1993.
[12]
Wolf, P.R. Elements of Photogrammetry, 2nd ed. ed.; McGraw-Hill: New York, NY, USA, 1983.
[13]
Zagrouba, E.; Barhoumi, W.; Amri, S. An efficient image-mosaicing method based on multifeature matching. Mach. Vision Appl. 2009, 20, 139–162.
[14]
Shindler, L.; Moroni, M.; Cenedese, A. Using optical flow equation for particle detection and velocity prediction in particle tracking. Appl. Math. Comput. 2012, 218, 8684–8694.
[15]
Hsieh, J.W. Fast stitching algorithm for moving object detection and mosaic construction. Image Vision Comput. 2004, 22, 291–306.
[16]
Im, J.; Jensen, J.R.; Tullis, J.A. Object-based change detection using correlation image analysis and image segmentation. Int. J. Remote Sens. 2008, 29, 399–423.
[17]
Nicolas, H. New methods for dynamic mosaicking. IEEE Trans. Image Process. 2001, 10, 1239–1251.
[18]
Nicolas, H.; Denoual, F. Semi-automatic modifications of video object trajectories for video compositing applications. Signal Process. 2005, 85, 1970–1983.
[19]
Pohl, C.; van Genderen, J.L. Multisensor image fusion in remore sensing: Concepts, methods and applications. Int. J. Remote Sens. 1998, 19, 823–854.
[20]
Raffel, M.; Willert, C.; Kompenhans, J. Particle Image Velocimetry; A Practical Guide; Springer: Berlin, Germany, 1998.
