The applications of neural network in mapping, modeling and change detection using remotely sensed data

Thesis (Ph.D.)--Boston UniversityAdvances in remote sensing and associated capabilities are expected to proceed in a number of ways in the era of the Earth Observing System (EOS). More complex multitemporal, multi-source data sets will become available, requiring more sophisticated analysis methods. This research explores the applications of artificial neural networks in land-cover mapping, forward and inverse canopy modeling and change detection. For land-cover mapping a multi-layer feed-forward neural network produced 89% classification accuracy using a single band of multi-angle data from the Advanced Solidstate Array Spectroradiometer (ASAS). The principal results include the following: directional radiance measurements contain much useful information for discrimination among land-cover classes; the combination of multi-angle and multi-spectral data improves the overall classification accuracy compared with a single multi-angle band; and neural networks can successfully learn class discrimination from directional data or multi-domain data. Forward canopy modeling shows that a multi-layer feed-forward neural network is able to predict the bidirectional reflectance distribution function (BRDF) of different canopy sites with 90% accuracy. Analysis of the signal captured by the network indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model shows that the R2 between the network-predicted canopy parameters and the actual canopy parameters is 0.85 for canopy density and 0.75 for both the crown shape and the height parameters. [TRUNCATED

Modelling bidirectional radiance measurements collected by the advanced solid-state array spectroradiometer over Oregon transect conifer forests

Thesis (M.A.)--Boston UniversityThe primary objective of this research is to test and validate a geometric-optical bidirectional reflectance canopy model developed by Li and Strahler, with respect to actual forest canopy reflectance measurments. This model treats forest canopies as scenes of discrete, three dimensional objects that are illuminated and viewed from different positions in the hemisphere. The shapes of the objects, their count densities and patterns of placement are the driving variables, and they condition the mixture of sunlit and shaded objects and background that is observed from a particular viewing direction, given a direction of illumination. This mixture, in turn, controls the brightness apparent to an observer or a radiometric instrument. The Advanced Solid-State Array Spectroradiometer (ASAS) is chosen to be the sensor having the ability of collecting measurements at various look angles and its imaged reflectance was used to validate the model. The modelled BRF's were compared to actual ASAS measured BRF's in sites with different canopy structures and densities. The comparision revealed execellent match between the modelled and measured reflectance, and great ability of the model in predicting the shape and magnitude of the BRDF, in almost all the sites investigated. It is concluded that the geometric optics approach provided a good way to model the bidirectional reflectance distribution function of natural vegetation canopies, that captures the most important features exhibited by bidirectional measurements of such canopies. Further modifications have been suggested that will improve the predicted BRF's, and yield better results. [TRUNCATED