Recent infrared (IR) sensors are mostly based on a focal-plane array (FPA) structure. However, IR images suffer from
the fixed pattern noise (FPN) due to non-uniform response of a FPA structure. Various nonuniformity correction (NUC)
techniques have been developed to alleviate the FPN. They can be categorized into reference-based and scene-based
approaches. In order to deal with a temporal drift, however, a scene-based approach is needed. Among scene-based
algorithms, conventional algorithms compensate only for the offset non-uniformity of IR camera detectors based on the
global motion information. Local motions in a video, however, can introduce inaccurate motion information for NUC.
Considering global and local motions simultaneously, we propose a correction algorithm of gain and offset. Experiment
results using simulated and real IR videos show that the proposed algorithm provides performance improvement on the
The road network is one of the most important types of information in the Geographic Information System (GIS).
However, automatic extraction of roads is still considered a challenging problem. In this paper, we focus on robust
extraction of main roads. In the proposed algorithm, we first determine the roadness of each pixel using the eigenvalues
of its Hessian matrix. The roadness represents the belongingness of a pixel to a road; and its determination is performed
on a multi-scale basis so that it is robust to various widths of roads. We then perform directional grouping to the
determined initial road map and remove outliers in each group via directionally morphological filtering. Finally, we
determine roads by combining the results from each group. Experimental results show that the proposed algorithm can
automatically extract most main roads in various remote sensing images.