Importance-weighted multi-scale texture and shape descriptor for object recognition in satellite imagery

We present a sliding window-based, per-pixel importance-weighted, multi-scale, cell-structured feature descriptor and demonstrate its performance for recognizing different aircraft from remotely sensed imagery. Opening and closing differential morphological profiles are constructed, then fused with the Choquet integral to create a soft segmentation. A per-pixel importance map is derived from the soft segmentation and used in the calculation of histogram of oriented gradients, local binary patterns, invariant object moments, and Haar-like features. Superiority is demonstrated in comparison to flat single-scale and non-importance weighted representations with encouraging results for both cross-validation and blind testing. Results show that the pyramid, cell-structured, importance-weighting performs better than traditional approaches in the difficult problem space of recognizing objects in remote sensing imagery.