Neural Networks for Land Cover Applications
Following upon their resurgence as a research topic in the second half of the eighties [1][2], neural networks burst out in the remote sensing community by the end of the same decade when a bunch of investigators started looking at them as an effective al
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Department of Information, Systems and Productions - Tor Vergata University [email protected] Aerospace Department - University of Colorado at Boulder
11.1 Neural Networks in Remote Sensing Following upon their resurgence as a research topic in the second half of the eighties [1][2], neural networks burst out in the remote sensing community by the end of the same decade when a bunch of investigators started looking at them as an effective alternative to more traditional methods for extracting information from data collected by airborne and spaceborne platforms [3] [4]. Indeed, for researchers used to handle multidimensional data, the possibility of building classification algorithms avoiding the assumptions required by the Bayesian methods on the data probability distributions seemed rather attractive. For the case of parameter retrieval, neural networks owned the advantage of determining the input-output relationship directly from the training data with no need to seek for an explicit modelling of the physical mechanisms, often nonlinear and with poorly understood factors involved [5]. Moreover, it was shown that multilayer feedforward networks formed a class of universal approximators, capable of approximating any real-valued continuous function provided a sufficient number of units in the hidden layer was considered [6]. There were enough reasons to explore the potentialities of such neurocomputational models, also known as associative memory based models, in a wide range of remote sensing applications. This was actually what happened throughout all nineties and, even if with less emphasis, is continuing up to date. Image classification, from SAR imagery to latest hyperspectral data, has probably been one of the most investigated fields in this context. However, the use of neural network for the retrieval of biogeophysical parameters from remotely sensed data has also played a significant role. In particular, the synergy between these algorithms and RT (Radiative Transfer) electromagnetic models represented a new and effective way to replace the empirical approaches, often based on limited seasonally or regionally data, hence with little generalization capabilities. The use of the RT models is twofold. They can help in the sensitivity analysis of the electromagnetic quantities to the bio-geophysical parameters. They can be exploited for the synthetic generation of training data in case of a lack of experimental data and ground-truth. The combined use of electromagnetic models and neural networks is a topic treated in many published studies taking into account the most diverse applicative scenarios: from the inversion of radiance spectra to infer atmospheric ozone concentration profiles [7], to the retrieval of snow parameters from passive microwave M. Gra˜ na and R.J. Duro (Eds.): Comput. Intel. for Remote Sensing, SCI 133, pp. 267–293, 2008. c Springer-Verlag Berlin Heidelberg 2008 springerlink.com
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F. Pacifici et al.
measurements [8] or to the estimation of sea water optically active parameters from
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