Thesis on change detection

Literature review land use land cover change

This necessitates development of automated remote sensing algorithms which can monitor large areas with minimal human intervention. This is the main goal of this thesis. However, manual monitoring using high resolution photography or field surveys can become very difficult and time consuming or even infeasible because such changes cover very large areas. These methods consider the intrinsic properties of the hyperspectral data and overcome the drawbacks of the existing CD techniques. PhD thesis, University of Trento. Moreover, a simple yet effective tool is developed allowing user to have an interaction within the CD procedure. Item Type:. To fully utilize the available multitemporal hyperspectral images and their rich information content, it is necessary to develop advanced techniques for robust change detection CD in multitemporal hyperspectral images, thus to automatically discover and identify the interesting and valuable change information. Reliable change variables are adaptively generated for the representation of each specific considered change. It models the hyper-temporal multi-spectral MODIS Vegetation Index VI time series with a triply modulated cosine function using a sliding window non-linear least squares and applies a change metric based on log-likelihood ratios to the trend parameter time-series of the fitted model, instead of the raw vegetation index.

This necessitates development of automated remote sensing algorithms which can monitor large areas with minimal human intervention. These methods consider the intrinsic properties of the hyperspectral data and overcome the drawbacks of the existing CD techniques.

The effectiveness of these approaches, to the complex CD problems is reduced, when dealing with the hyperspectral images.

land use and land cover change pdf

To fully utilize the available multitemporal hyperspectral images and their rich information content, it is necessary to develop advanced techniques for robust change detection CD in multitemporal hyperspectral images, thus to automatically discover and identify the interesting and valuable change information.

This helps in avoiding the Gaussian assumption about the data, which is a major drawback of the traditional Bayesian Classifiers e. Item Type:.

This is the main goal of this thesis.

landuse landcover change detection pdf

This important property makes it possible the monitoring of the land-cover dynamic and environmental evolution at a fine spectral scale.

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