KEYWORDS: 3D modeling, 3D image processing, Data modeling, Image processing, Spatial resolution, 3D image reconstruction, Clouds, Roads, LIDAR, Image fusion
The development of digital aerial camera provides the possibility of acquiring highly overlapped aerial images with high
spatial resolution. In addition to its high spatial resolution that improves the capability of image interpretation, the highly
overlapped images provide favorable geometrical configuration with high redundancy. The high similarity of stereo
images is, thus, beneficial to the reliable image matching. Hence, the 3-D point clouds from the image matching have the
great potential in 3-D modeling. The topographic maps provide the distinct boundaries for building modeling. The
strategies of building reconstruction with existing topographic maps may improve the quality, cost, and efficiency for
building modeling. The objective of this investigation is to integrate highly overlapped aerial images and building
boundaries from topographic map. The proposed semi-automatic method includes 3-D lines extraction and polyhedral
model generation. In the beginning, the operator locates the initial lines by a graphical user interface. The initial lines are
refined by stereo images. The precise 3-D lines are processed into a 3-D modeling by an inference engine. The
experimental results indicate that the proposed method may reconstruct the 3-D building model effectively.
Image fusion is an important technique to integrate high resolution panchromatic image and low resolution multispectral
image. The fused image enhances the capability for image interpretation. EMD (Empirical Mode Decomposition) is an
effective method to decompose the linear or nonlinear signals into a set of IMFs (Intrinsic Mode Functions). The
characteristics of EMD may apply to image fusion technique. The objective of this investigation is to establish a novel
image fusion method using a 2-D EMD. The idea of image fusion based on EMD is to decompose the panchromatic and
multispectral images into their IMFs. Then, we replace the high frequency IMF of multispectral image by high frequency
IMF of panchromatic image. Finally, the image fusion is performed by reconstructing the mixed IMFs. The experimental
results indicate that the proposed method may produce a fused image that preserves spatial and spectral information.
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