Picture of neon light reading 'Open'

Discover open research at Strathprints as part of International Open Access Week!

23-29 October 2017 is International Open Access Week. The Strathprints institutional repository is a digital archive of Open Access research outputs, all produced by University of Strathclyde researchers.

Explore recent world leading Open Access research content this Open Access Week from across Strathclyde's many research active faculties: Engineering, Science, Humanities, Arts & Social Sciences and Strathclyde Business School.

Explore all Strathclyde Open Access research outputs...

Pixel clustering and hyperspectral image segmentation for ocean colour remote sensing

Zeng, Xuexing and Ren, Jinchang and McKee, David and Lavender, Samantha and Marshall, Stephen (2012) Pixel clustering and hyperspectral image segmentation for ocean colour remote sensing. In: Hyperspectral Imaging Conference, 2012-05-15 - 2012-05-16.

Full text not available in this repository. Request a copy from the Strathclyde author

Abstract

Hyperspectral dataset classification is a basic task for ocean colour remote sensing [McKee et al., 2007], [Borengasser et al., 2004]. In this paper, region growing is proposed to classify hyperspectral dataset [Adams et al., 1994]. Considering that it is difficult to select seeds, we select 20 by 20 uniformly distributed seeds for region growing. The region will grow from the seed by adding its 4-connected neighbours that is most similar with mean value vector. The Euclidean distance is used to measure the similarity between pixels. If the Euclidean distance between mean value vector and neighbour of seed is smaller than the threshold, this neighbour is considered that it is similar with this grown region, and this neighbour will be added to this growing region. Good classification results can be obtained by simply adjusting similarity threshold to specify the pixel similarity to preserve more or less details in the segmented results. With another parameter: size threshold for post- processing, the results can be further refined. One band sample of dataset and the classification results with threshold: 0.001 are shown in Figure 1, and more results will be presented in the full paper.