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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. 2 of 28 descriptor (PIIFD) [13], R-SIFT [14] orientation-restricted SIFT (OR-SIFT) [15], and multimodal SURF (MM-SURF) [16] use gradient orientation modification to limit the gradient orientation to (0, pi) on the basis of the intensity reversal in certain areas. 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.Even though these descriptors perform slightly better than the traditional descriptors, the number of mismatches increases due to the orientation reversal, and the total number of matched points is still low. This is because the description ability of these descriptors relies on a linear relationship between images, and they are not appropriate for the significant non-linear intensity differences caused by the radiometric variations between multi-sensor images.Some descriptors have been designed based on the distribution of edge points, which can be regarded as the common features of multi-sensor images. Aguilera et al. [18] proposed the edge-oriented histogram (EOH) descriptor for multispectral images. Li et al. [19] assigned the main orientation computed with PIIFD to EOH for increased robustness to rotational invariance. Zhao et al. [20] used edge lines for a better matching precision. Shi et al. [21] combined shape context with the DAISY descriptor in a structural descriptor for multispectral remote sensing image registration; however, all the edge points are constrained by contrast and threshold values [22]. Other descriptors have been proposed, based on loca...