Vegetation Segmentation for Sensor Fusion of Omnidirectional Far-Infrared and Visual Stream

In the context of vegetation detection, the fusion of omnidirectional (O-D) infrared (IR) and color vision sensors may increase the level of vegetation perception for unmanned robotic platforms. Current approaches are primarily focused on O-D color vision for localization, mapping, and tracking. A literature search found no significant research in our area of interest. The fusion of O-D IR and O-D color vision sensors for the extraction of feature material type has not been adequately addressed. We will look at augmenting indices-based spectral decomposition with IR region-based spectral decomposition to address the number of false detects inherent in indices-based spectral decomposition alone. Our work shows that the fusion of the normalized difference vegetation index (NDVI) from the O-D color camera fused with the IR thresholded signature region associated with the vegetation region minimizes the number of false detects seen with NDVI alone. The contribution of this paper is the demonstration of a new technique, thresholded region fusion technique for the fusion of O-D IR and O-D color. We also look at the Kinect vision sensor fused with the O-D IR camera. Our experimental validation demonstrates a 64% reduction in false detects in our method compared to classical indices-based detection.