Effective Hyper-Spectral Image Segmentation Using Multi-scale Geometric Analysis

The wide availability of multispectral images has fostered the development of new algorithms for remote sensing applications. These applications range from agricultural and environmental to military use. Nevertheless, the analysis of such voluminous data requires advanced analysis and computational methodologies as well as advanced hardware and computational methods. In this paper we introduce a new state-of-the-art method for segmentation of Hyperspectral images which uses both spectral and spatial information simultaneously. The proposed methodology is based on a multiscale geometric transformation, called the Beamlet Transform, and the Beamlet Decorated Recursive Dyadic Partitioning (BDRDP). The method is applicable for both mono-spectral and multispectral images where each pixel has its corresponding spectral profile vector. The proposed segmentation method is especially effective when the underlying image consists of relatively large segments with smooth boundaries. In this case, it performs exceptionally well even when the Signal to Noise Ratio (SNR) is extremely low. The method is unsupervised and assumes no prior knowledge of the image characteristics or features. Furthermore, it involves a free sensitivity parameter which allows fine tuning for a specific application, and thus improving segmentation results. Despite of being relatively complex and sophisticated, the proposed segmentation algorithm has a low computational complexity of . This is achieved by implicit computations through the Pseudo-Polar Fast Fourier transform (PPFFT). In order to validate the efficiency of our method, we have used the Lark algorithm which also combines spectral and spatial analysis but lacks the multi-scale property, for segmentation of multi-spectral images and compared its performance to the method proposed in this paper. These comparisons showed that our new proposed method outperforms the Lark algorithm and emphasized the effectiveness of multi-scale analysis. The proposed method was successfully applied to real aerial multi-spectral imagery for the application of estimating nitrogen levels in agricultural areas.