Spectral/spatial data fusion and neural networks for vegetation understory information extraction from hyperspectral airborne images

Elisabetta Binaghi; Ignazio Gallo; Mirco Boschetti; Pietro Alessandro Brivio

doi:10.1117/12.517887

5 February 2004 Spectral/spatial data fusion and neural networks for vegetation understory information extraction from hyperspectral airborne images

Elisabetta Binaghi, Ignazio Gallo, Mirco Boschetti, Pietro Alessandro Brivio

Author Affiliations +

Proceedings Volume 5238, Image and Signal Processing for Remote Sensing IX; (2004) https://doi.org/10.1117/12.517887
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

In this paper, we propose a method able to fuse spectral information with spatial contextual information in order to solve “operationally” classification problem. The salient aspect of the method is the integration of heterogeneous data within a Multi-Layer Perceptron model. Spatial and spectral relationships are not explicitly formalized in an attempt to limit design and computational complexity; raw data are instead presented directly as input to the neural network classifier. The method in particular addresses new open problems in processing hyperspectral and high resolution data finding solution for multisource analysis. Experimental results in real domain show this fusing approach is able to produce accurate classification. The method in fact is able to handle the problem of a volumetric mixture typical of natural forest ecosystems identifying the different surfaces present under the tree canopy. The understory map, produced by the neural classification method, was used as input to the inversion of radiative transfer models that show a significant increase in the retrieval of important biophysical vegetation parameter.

Citation Download Citation

Elisabetta Binaghi, Ignazio Gallo, Mirco Boschetti, and Pietro Alessandro Brivio "Spectral/spatial data fusion and neural networks for vegetation understory information extraction from hyperspectral airborne images", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); https://doi.org/10.1117/12.517887

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KEYWORDS

Remote sensing

Neural networks

Data fusion

Image fusion

Image resolution

Vegetation

Data modeling

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