0000000000627700
AUTHOR
Julia Amoros
Multiset Kernel CCA for multitemporal image classification
The analysis of multitemporal remote sensing images is becoming an increasingly important problem because of the upcoming scenario of multispectral satellite constellations monitoring our Planet. Algorithms that can analyze such amount of heterogeneous information are necessary. While linear techniques have been extensively deployed, this work considers a kernel method that finds nonlinear correlations between all image sources and the class labels. We introduce in this context the Kernel Canonical Correlation Analysis (KCCA) to exploit the wealth of temporal image information and to handle nonlinear relations in a natural way via kernels. To achieve this goal, we use the generalization of …
Biophysical parameter estimation with adaptive Gaussian Processes
We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of ea…
Cloud detection for CHRIS/Proba hyperspectral images
Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant source of error in both sea and land cover biophysical parameter retrieval. Sensors with spectral channels beyond 1 um have demonstrated good capabilities to perform cloud masking. This spectral range can not be exploited by recently developed hyperspectral sensors that work in the spectral range between 400- 1000 nm. However, one can take advantage of their high number of channels and spectral resolution to increase the cloud detection accuracy, and to describe properly the detected c…