HOMPC: A Local Feature Descriptor Based on the Combination of Magnitude and Phase Congruency Information for Multi-Sensor Remote Sensing Images

Abstract

Local region description of multi-sensor images remains a challenging task in remote sensing image analysis and applications due to the non-linear radiation variations between images. This paper presents a novel descriptor based on the combination of the magnitude and phase congruency information of local regions to capture the common features of images with non-linear radiation changes. We first propose oriented phase congruency maps (PCMs) and oriented magnitude binary maps (MBMs) using the multi-oriented phase congruency and magnitude information of log-Gabor filters. The two feature vectors are then quickly constructed based on the convolved PCMs and MBMs. Finally, a dense descriptor named the histograms of oriented magnitude and phase congruency (HOMPC) is developed by combining the histograms of oriented phase congruency (HPC) and the histograms of oriented magnitude (HOM) to capture the structure and shape properties of local regions. HOMPC was evaluated with three datasets composed of multi-sensor remote sensing images obtained from unmanned ground vehicle, unmanned aerial vehicle, and satellite platforms. The descriptor performance was evaluated by recall, precision, F1-measure, and area under the precision-recall curve. The experimental results showed the advantages of the HOM and HPC combination and confirmed that HOMPC is far superior to the current state-of-the-art local feature descriptors.