Assimilation of endmember variability in spectral mixture analysis for urban land cover extraction

Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember variability in the linear mixture model by incorporating the variance for each class, the signals of which varies from pixel to pixel due to change in urban land cover (LC) structures. VECLS is first tested with a computer simulated three class endmember considering four bands having small, medium and large variability with three different spatial resolutions. The technique is next validated with real datasets of IKONOS, Landsat ETM+ and MODIS. The results show that correlation between actual and estimated proportion is higher by an average of 0.25 for the artificial datasets compared to a situation where variability is not considered. With IKONOS, Landsat ETM+ and MODIS data, the average correlation increased by 0.15 for 2 and 3 classes and by 0.19 for 4 classes, when compared to single endmember per class.     

Direct-Ranging Loran Model Identification and Performance Predictions

With the availability of modern data processing techniques and lowcost stable time references, the use of Loran in a direct-ranging mode offers certain potential advantages. In order to generate reliable direct-ranging Loran (DRL) performance projections and system designs, however, accurate system error models are required. This paper first describes the processing of airborne flight data from a DRL receiver to identify models for significant DRL system errors. These models are then used in a Kalman filter covariance simulation to generate performance predictions for an optimally integrated DRL system. Comparisons with conventional hyperbolic Loran are also given. DRL is shown to be capable of substantially improved position accuracy over that of conventional hyperbolic Loran, especially in regions of poor geometry. A stationary ground-align technique for improving DRL performance is also discussed.