Geometric feature descriptor and dissimilarity-based registration of remotely sensed imagery.

ABSTRACT: Image registration of remotely sensed imagery is challenging, as complex deformations are common. Different deformations, such as affine and homogenous transformation, combined with multimodal data capturing can emerge in the data acquisition process. These effects, when combined, tend to compromise the performance of the currently available registration methods. A new image transform, known as geometric mean projection transform, is introduced in this work. As it is deformation invariant, it can be employed as a feature descriptor, whereby it analyzes the functions of all vertical and horizontal signals in local areas of the image. Moreover, an invariant feature correspondence method is proposed as a point matching algorithm, which incorporates new descriptor's dissimilarity metric. Consi

SUBMITTER: Kahaki SMM 

PROVIDER: S-EPMC6067644 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature