A novel multi-index learning approach for urban classification of high-resolution images

In this paper, a multi-index learning (MIL) approach is proposed to represent and classify the complex urban scenes using a set of low-dimension information indices instead of the traditional high-dimensional spatial features. Specifically, two categories of indices are proposed: 1) Primitive indices (PI), involving a series of basic urban primitives, e.g., buildings, shadow, vegetation; and 2) Variation indices (VI), describing the spectral and spatial variation of the urban scenes. Experiments conducted on a large-scale image (260 km²) captured by the ZY3 satellite (the first Chinese civilian high-resolution satellite) show that the proposed MIL approach can provide promising accuracies even though the complicated urban landscape is represented via low-dimensional feature space. The satisfactory results achieved by the MIL can be attributed to the low-dimensional but high-level semantic information considered.