Object-based VHSR image classification using multiband compact texture unit descriptor

In remote sensing, texture is commonly used to support spectral information particularly when spectral signatures of class of interest are similar. It is usually extracted using panchromatic band instead of multispectral bands. This is because panchromatic band has rich texture content due to its fine spatial resolution. Recent space-borne and pansharpening techniques can deliver multispectral images with a submetric resolution which are also good candidates for texture analysis. The difficulty in extracting texture in
multispectral images is the fact that existing and widely used methods are limited to analyzing spatial relationship between pixels in a single band at a time. When multispectral images are used texture characterization is usually performed by analyzing spatial relationships in each spectral band independently. This ignores inter-band spatial relationships which can be a source of valuable source of information.
This paper evaluates the capability of a recently proposed method named multiband compact texture unit. This method extracts texture by characterizing simultaneously spatial relationship in the same band and across the different bands. This evaluation is performed in the context of object-based classification paradigm using WorldView-2 image of a forest area. For that image-objects were generated through superpixel segmentation. Classification in the object-feature space is performed suing K nearest neighbor
algorithm. The proposed approach is compared to two groups of methods. The first group includes texture methods that use only spatial relationships in the same band: Gabor features wavelets and Granulometry. The second group includes methods that use intra-band and inter-band spatial relationships: integrative gray-level co-occurrence matrix, opponent Gabor features and opponent local binary patterns.
Experimental results show that texture extracted using both intra-band and inter-band spatial relationship improves the classification accuracy compared to when it is extracted in each spectral band independently. Among the methods of the second group that use both intra-band and inter-band spatial relationships, the multiband compact texture unit method produces the best results.