An automated waveband selection technique for optimized hyperspectral mixture analysis

• Published in: International journal of remote sensing Vol. 31; no. 20; pp. 5549 - 5568
• Main Authors: Somers, B., Delalieux, S., Verstraeten, W. W., van Aardt, J. A. N., Albrigo, G. L., Coppin, P.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 20.10.2010
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Automated; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Remote sensing; Teledetection and vegetation maps; algorithms; spectra; components; Protocols; Spatial variability; Spectral band; urban environment; Index; Mixture; automation; optimization; Selection criterion; Ratio; Ability; news; remote sensing; utilization; urban areas; analysis; in situ; techniques; Hyperspectral characteristic; instability; Problem; Pixel
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160903311305

Linear spectral mixture analysis (SMA) has been used extensively in remote sensing studies to estimate the sub-pixel composition of spectral mixtures. The lack of ability to account for sufficient temporal and spatial variability between and among ground component or endmember spectra has been acknowledged as a major shortcoming of conventional SMA approaches. In an attempt to overcome this problem, a novel and automated linear spectral mixture protocol, referred to as stable zone unmixing (SZU), is presented and evaluated. Stable spectral features (i.e. least sensitive to spectral variability) are automatically selected for use in the mixture analysis based on a minimum InStability Index (ISI) criterion. ISI is defined as the ratio of the spectral variability within and the spectral variability among the endmember classes that are present within the mixture. The algorithm was tested on a set of scenarios, generated from in situ measured hyperspectral data. The scenarios covered both urban and natural environments under differing conditions. SZU provided reliable endmember cover distribution maps in all scenarios. On average, an absolute gain in R 2 -the coefficient of determination of the modelled versus the observed sub-pixel cover fractions-of 0.14 over the traditional SMA approaches was observed while the absolute gain in fraction abundance error was 0.06. It was concluded that the SZU protocol has potential to be an effective and efficient SMA algorithm for generating optimal cover fraction estimates regardless of the scenario considered. Moreover, the subset selection protocol, as implemented in SZU, can be regarded as complementary to conventional SMA approaches resulting in a further reduction of spectral variability.