ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 101-108, 2016
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-8/101/2016/
doi:10.5194/isprs-annals-III-8-101-2016
 
07 Jun 2016
OPTIMAL WAVELENGTH SELECTION ON HYPERSPECTRAL DATA WITH FUSED LASSO FOR BIOMASS ESTIMATION OF TROPICAL RAIN FOREST
T. Takayama1,2 and A. Iwasaki1 1Dept. of Advanced Interdisciplinary Studies, University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo, Japan
2Mitsubishi Research Institute, Inc., 2-10-3, Nagatacho, Chiyoda-ku, Tokyo, Japan
Keywords: Biomass, Hyperspectral, Forest Management, Fused Lasso, GLCM Abstract. Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous large-area forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the cross-validation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.
Conference paper (PDF, 1904 KB)


Citation: Takayama, T. and Iwasaki, A.: OPTIMAL WAVELENGTH SELECTION ON HYPERSPECTRAL DATA WITH FUSED LASSO FOR BIOMASS ESTIMATION OF TROPICAL RAIN FOREST, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 101-108, doi:10.5194/isprs-annals-III-8-101-2016, 2016.

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