Deep Modality Independent Descriptor Learning for Optical and SAR Image Patch Matching

Due to the complementary information between multi-modal images, they are widely used in various applications. However, there are significant differences in appearance caused by different imaging mechanisms, which bring great challenges to multi-modal image patch matching. To solve this problem, this paper proposes a deep modality independent descriptor learning network (DMID-Net) for multi-modal image patch matching. DMID-Net computes the self-similarity of deep features as the structure descriptor for image patch matching, which is independent of image modality and shared between multi-modal images. Thus, the acquired deep modality independent descriptor(DMID) can reduce the influence of significant differences between multi-modal images, further improving the matching performances. Experimental results on a large number of optical and SAR image-pairs demonstrate the effectiveness of DMID-Net on multi-modal image patch matching.