Unsupervised hyperspectral image segmentation: Merging spectral and spatial information in boundary adjustment

• Published in: 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) pp. 1466 - 1471
• Main Authors: Mughees, Atif, Xiaoqi Chen, Linmi Tao
• Format: Conference Proceeding
• Language: English
• Published: The Society of Instrument and Control Engineers - SICE 01.09.2016
• Subjects: Adaptive boundary adjustment; Clustering algorithms; Correlation; Hyperspectral imaging; Image segmentation; majority class computation; Partitioning algorithms; Spatial resolution; spatial-spectral contextual value; spectral-spatial correlation; unsupervised hyperspectal image segmentation
• DOI: 10.1109/SICE.2016.7749195

Hyperspectral Image (HSI) segmentation is one of the key prepossessing steps for subsequent applications and analysis. Unsupervised Segmentation without dimension reduction is a pervasive problem in Computer vision and a more challenging problem in HSI. A novel unsupervised spectral-spatial adaptive boundary adjustment/movement-based model and a framework is devised for HSI segmentation, which is initialized via weighted clustering. This model uses two key features of HSI data: spectral correlation and band spatial/contextual preference. Band preference is defined by the discriminative ability and informative amount contained in each band, and an adaptive mechanism is proposed to retain the spectral correlations in the spectral dimension and to match the actual structure in spatial dimensions. The proposed framework allows the image to be explored at different segmentation levels. Use of local structural regularity and self-similarity information from channel-group adaptive boundary adjustment based process, and final segmentation result based on the proposed four merge criteria's has a significant effect on the accuracy of the final segmentation. Experiments on real diverse hyperspectral data sets with different contexts and resolutions demonstrate the accuracy of the proposed framework over several well-known existing approaches and even proves its accuracy over the ground truth.