Spectral Information Retrieval for Sub-Pixel Building Detection

Abstract

Building extraction from imagery has been an active research area for decades. However, the precise building detection from hyperspectral (HSI) images solely is a less often addressed research question due to the low spatial resolution of data. The building boundaries are usually represented by spectrally mixed pixels, and classical edge detector algorithms fail to detect borders with sufficient completeness. The idea of the proposed method is to use fraction of materials in mixed pixels to derive weights for adjusting building boundaries. The building regions are detected using seeded region growing and merging in a HSI image; for the initial seed point selection the digital surface model (DSM) is used. Prior to region growing, the seeds are statistically tested for outliers on the basis of their spectral characteristics. Then, the border pixels of building regions are compared in spectrum to the seed points by calculating spectral dissimilarity. From this spectral dissimilarity the weights for weighted and constrained least squares (LS) adjustment are derived. We used the Spectral Angle Mapper (SAM) for spectral similarity measure, but the proposed boundary estimation method could benefit from soft classification or spectral unmixing results. The method was tested on a HSI image with spatial resolution of 4 m, and buildings of rectangular shape. The importance of constraints to the relations between building parts, e.g. perpendicularity is shown on example with a building with inner yards. The adjusted building boundaries are compared to the laser DSM, and have a relative accuracy of boundaries 1/4 of a pixel.