My goal is to detect roads from high resolution aerial images. I would like to adopt some kind of cascade classification (Haar, LBP) but I am stuck on some key decisions:
- Overall, is cascade approach fine for this kind of detection ? I found tons of examples related to object detection, but in my case objects are roads that cannot be represented with rectangles containing detected object;
- I have 3 channel images with 16 bit each channel (16*3=48 bit total). Usual viewers cannot display such kind of images (I tried also Gimp without success). So how positive samples should be created manually ? Moreover cropping could led to resolution loss. Is this a problem ?
- Which is the optimal contents for positive images ? Just road texture or larger images that contain roads ? Positive samples must have same size ?
- I have ground truth data made of a set of points that represent roads as lines and junctions between crossing lines. Maybe this data can replace image cropping for training ?
thanks