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Multimodal remote sensing image matching is still a challenging task, and its matching performance will highly influence the subsequent applications, such as image fusion, image mosaic and change detection. To tackle this problem, we propose a radiometric and rotational invariant descriptor (RRID) using the monogenic signal for multimodal image matching. First of all, a multiscale feature asymmetry measurement is proposed to obtain more reliable detected points, in which the multiscale feature asymmetry can be computed from the intermediate results from the monogenic signal; then a multiscale local phase orientation computation algorithm is designed for the main orientation assignment of each keypoint, in which the local phase orientation can also be provided by the monogenic signal construction; finally, a circular local window around each keypoint is extracted, a histogram of local phase orientations weighted by phase congruency is calculated for the log-polar feature description. RRID is compared with other state-of-the-art multimodal image matching methods on three publicly available datasets. Experimental results have demonstrated that our proposed method outperforms the compared methods in terms of precision, recall, and F1-measure criteria and also has achieved the lowest RMSEs. [ABSTRACT FROM AUTHOR]

Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time. [ABSTRACT FROM AUTHOR]