Automatic and unsupervised building extraction in complex urban environments from multi spectral satellite imagery

This paper presents an approach for building extraction in remotely sensed images composed of low-resolution multi-spectral and high resolution panchromatic bands. The proposed approach exploits spectral properties in conjunction with spatial properties, both of which actually provide complementary information to each other. First, high resolution pan-sharpened color image is obtained via the process of merging high resolution panchromatic and low resolution multispectral imagery yielding a color image at the resolution of panchromatic band. Natural and man-made regions are classified by using Normalized Difference Vegetation Index (NDVI). Then shadow is detected by using chromaticity to intensity ratio in YIQ color space. After the classification of the vegetation and the shadow areas, the rest of the image consists of man-made areas only. Then, the manmade areas are partitioned by mean shift segmentation. However, some resulting segments are irrelevant to buildings in shape. These artifacts are eliminated in two steps: First, each segment is thinned using morphological operations and the length of it is compared to a threshold which is specified according to the empirical length of buildings. As a result, long segments which most probably represent roads are masked out. Second, the erroneous thin artifacts are removed via principle component analysis (PCA). In parallel to PCA, small artifacts are wiped out based on morphological processes also. The resultant manmade mask image is overlaid on the ground truth image, where the buildings are manually labeled, for the assessment of the methodology. The proposed methodology is applied to various Quickbird images. The experiments show that the methodology performs well to extract buildings in complex environments.