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Hyperspectral imaging has been widely used for agriculture, astronomy, surveillance, and so on. However, hyperspectral imaging usually suffers from low‐spatial resolution, due to the limited photons in individual bands. Recently, more hyperspectral image super‐resolution methods have been developed by fusing the low‐resolution hyperspectral image and high‐resolution RGB image, but most of them did not consider the misalignment between two input images. In this study, the authors present an effective method to restore a high‐resolution hyperspectral image from the misaligned low‐resolution hyperspectral image and high‐resolution RGB image, which exploits spectral and spatial correlation in hyperspectral and RGB images. Specifically, they employ the spectral sparsity to restore the high‐resolution hyperspectral image on the misaligned part, and then simultaneously employ spectral and spatial structure correlation to restore the high‐resolution hyperspectral image on the aligned area, which can be fused to obtain the high‐quality hyperspectral image restoration under a misaligned hybrid camera system. Experimental results show that the proposed method outperforms the state‐of‐the‐art hyperspectral image super‐resolution methods under a misaligned hybrid camera system in terms of both objective metric and subjective visual quality.

The lane detection is important for the autonomous vehicle vision navigation used in the intelligent transportation system (ITS). Several approaches for lane detection were suggested in the past. However, there is still one issue about robustness. This paper presents a robust and real-time multi-lane detection method based on Road Marking Feature Points (RMFP) for autonomous vehicle navigation in urban environment. The key idea is to apply methods from extracting RMFP and the target tracking domain to identify lanes information in this article. Then we extract the RMFP from the gray-scale image and the IPM image. Besides we also use the lane line color and structure features to sift RMFP that meets lane line. At last, we adopt the clustering method to generate lane lines, and we track these lines by frame association and Kalman Filter. The experimental results show that our proposed method is robust and real-time detect the lane line of various kinds of complicated road. And based on the lane line visual navigation of unmanned experiment validate the reliability of our method.

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