This presentation reports the laboratory tests and calibrations of a novel electro-optically tunable Lyot filter for imaging spectro - polarimetry. The filter’s high-resolution bandpass (λ/▵λ = 3.5 E+3) is centred on filter Fe XIV, 530 3 nm (green line) solar corona emission line Performances of this Liquid Crystal Tunable filter and Polarimeter Results F-P Etalon 530.3 line. Tunable-(LCTP) are also compared with the performances of a classical very high-resolution (λ/▵λ = 2.5 E+4) Fabry-Perot (FP) etalon. The results of the tests show even if the F-P etalon has a higher instrumental resolution, the LCTP has the same effective spectral resolution thanks to its electro-optical fine-tuning capability in wavelength. In addition, the LCTP has the advantage over the F-P etalon of having no mechanically moving parts, polarimetric capability and to do imaging. This project is part of the ESA Startiger activities and part of the preliminary studies for the ASPIICS coronagraph for the PROBA-3 formation flying mission.
We present a fast pan-sharpening method, namely FWLS, which is based on unsupervised segmentation of the
original multispectral (MS) data for improved parameter estimation in a weighted least square fusion scheme.
The use of simple thresholding of the normalized difference vegetation index (NDVI) dramatically reduces the
computation time with respect to the recently proposed WLS method which is based on accurate supervised
classification through kernel support vector machines. The fusion performances of the FWLS algorithm are the
same that those obtained by the WLS algorithm, and even higher in some cases, since accurate extraction of
vegetated/non-vegetated areas is only needed and high-performance supervised classification is generally not required
for fusion parameter estimation. Experimental results and comparisons to state-of-the-art fusion methods
are reported on Ikonos and QuickBird data. Both visual and objective quality assessment of the fusion results
confirm the validity of the proposed FWLS algorithm.
The Orthogonal Subspace Projection (OSP) algorithm is substantially a kind of matched filter that requires the
evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated
improved results for target detection in hyperspectral images. The use of kernel methods helps to combat the
high dimensionality problem and makes the method robust to noise. This paper incorporates the contextual
information to KOSP with a family of composite kernels of tunable complexity. The good performance of the
proposed methods is illustrated in hyperspectral image target detection problems. The information contained in
the kernel and the induced kernel mappings is analyzed, and bounds on generalization performance are given.
Kernel-based Orthogonal Subspace Projection (KOSP) provides good results in the field of classification of
hyperspectral images. However, an open-problem is the evaluation from the ground-truth samples of the
prototypes that best represent the classes. In the original formulation of KOSP, this preliminary (training)
stage is very simple since for each class the prototype is computed as the centroid of the ground-truth samples.
In order to improve KOSP performances, in this paper we introduce a minimization problem to evaluate the
best prototypes from a given ground truth of a specific classification problem. K-fold cross-validation is used to
avoid overfitting. The performance of the proposed methodology is tested by classifying the widely used 'Indian
Pine' hyperspectral dataset collected by the AVIRIS spectrometer.
Proc. SPIE. 6365, Image and Signal Processing for Remote Sensing XII
KEYWORDS: Hyperspectral imaging, Agriculture, Detection and tracking algorithms, Data modeling, Sensors, Image processing, Remote sensing, Image classification, Prototyping, Simulation of CCA and DLA aggregates
The aim of this paper is to assess and compare the performance of two kernel-based classification methods based
on two different approaches. On one hand the Support Vector Machine (SVM), which in the last years has shown
excellent results for hard classification of hyperspectral data; on the other hand a detection method called Kernel Orthogonal Subspace Projection KOSP, proposed in a recent paper.1 To this aim, the widely used "Indian Pine" Aviris dataset is adopted, and a common "test protocol" has been considered: both methods have been tested adopting the one-vs-rest strategy, i.e. by performing the detection of each spectral signature (representing one of the N classes) and by considering the spectral signatures of the remaining N - 1 classes as background. The same dimensionality of the training set is also considered in both approaches.