Abstract
An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however their performance is highly dependent on the amount of available ground truth. This is a problem because it is generally expensive to obtain ground truth information due to difficulties and costs associated with sample collection and analysis. In this paper, we present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data owing to their resilience to spectral variabilities. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. We experimentally demonstrate the efficacy of the proposed methods against existing approaches on three real-world hyperspectral datasets and one synthetic dataset.
Highlights
Vegetation parameter estimation is the problem of retrieving information about the biochemical quantities or the biophysical properties of the vegetation from its reflectance spectrum [1]
Most of the previous studies have used the squared exponential covariance function but we show that spectral metrics-based covariance functions provide better priors for vegetation parameter retrieval, especially under limited ground truth and illumination variations
Our results prove that when applying Gaussian processes for vegetation parameter estimation, rather than just utilizing the default squared exponential covariance function as done by previous studies, it would be wise to use model selection techniques, such as cross validation, to choose the best non-stationary covariance function
Summary
Vegetation parameter estimation is the problem of retrieving information about the biochemical quantities (e.g., concentration of photosynthetic pigments and plant nutrients) or the biophysical properties (e.g., fractional vegetation cover, water stress, and biomass) of the vegetation from its reflectance spectrum [1]. These methods mostly do not require expert knowledge about spectral features as required for designing VIs and RTM models They are much more flexible in that they can be used to predict a variety of vegetation parameters provided adequate ground truth is available, unlike traditional approaches which are generally specific to a set of vegetation parameters. The biggest challenge for using deep architectures for vegetation parameters estimation is that the number of parameters of such models can be very large for high dimensional signal, such as hyperspectral spectra, which can lead to model over-fitting if large amount of training data is unavailable To tackle this issue, Ni et al [20] proposed an “importance factor block” that weights important bands in the spectra, essentially performing a dimensionality reduction, before passing it as input to a one-dimensional convolutional network for prediction.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
