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.
Highlights
With the rapid development of sensor technology and modern communications, we are entering a multi-sensor era
The log-Gabor histogram descriptor (LGHD) descriptor performs much better than the remaining descriptors, and partial intensity invariant feature descriptor (PIIFD), edge-oriented histogram (EOH), phase congruency and edge-oriented histogram descriptor (PCEHD), and multispectral feature descriptor (MFD) perform better than local self-similarity (LSS), speeded-up robust features (SURF), LSS, and NG-scale-invariant feature transform (SIFT), all of which present similar results
We proposed a novel descriptor based on the combination of magnitude and phase congruency information to capture the common information of multi-sensor images with non-linear radiation variations
Summary
With the rapid development of sensor technology and modern communications, we are entering a multi-sensor era. The traditional approaches based on histograms of oriented gradient descriptors such as scale-invariant feature transform (SIFT) [11] and speeded-up robust features (SURF) [12] perform well on single-sensor images, but generate only a few correct mappings when dealing with multi-sensor images. To address this issue, researchers have proposed many techniques to adapt descriptors based on SIFT/SURF to multi-sensor images. Saleem et al [17] proposed NG-SIFT, which employs a normalized gradient to construct the feature vectors, and it was found that NG-SIFT outperformed SIFT on visible and near-infrared images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
