Band selection (BS) has advantages over data dimensionality in satellite communication and data transmission in the sense that the spectral bands can be tuned by users at their discretion for data analysis while keeping data integrity. However, to materialize BS in such practical applications several issues need to be addressed. One is how many bands required for BS. Another is how to select appropriate bands. A third one is how to take advantage of previously selected bands without re-implementing BS. Finally and most importantly is how to tune bands to be selected in real time as number of bands varies. This paper presents an application in spectral unmixing, progressive band selection in linear spectral unmixing to address the above-mentioned issues where data unmixing can be carried out in a real time and progressive fashion with data updated recursively band by band in the same way that data is processed by a Kalman filter.
[1]
Thomas Kailath,et al.
Linear Systems
,
1980
.
[2]
J. Settle,et al.
Linear mixing and the estimation of ground cover proportions
,
1993
.
[3]
Chein-I Chang,et al.
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
,
2001,
IEEE Trans. Geosci. Remote. Sens..
[4]
Chein-I Chang,et al.
Further results on relationship between spectral unmixing and subspace projection
,
1998,
IEEE Trans. Geosci. Remote. Sens..
[5]
Chein-I Chang,et al.
Constrained subpixel target detection for remotely sensed imagery
,
2000,
IEEE Trans. Geosci. Remote. Sens..