Browsing Natural Sciences and Mathematics by Subject "Land use, Urban."
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Classification of Urban features using Airborne Hyperspectral DataAccurate mapping and modeling of urban environments are critical for their efficient and successful management. Superior understanding of complex urban environments is made possible by using modern geospatial technologies. This research focuses on thematic classification of urban land use and land cover (LULC) using 248 bands of 2.0 meter resolution hyperspectral data acquired from an airborne imaging spectrometer (AISA+) on 24th July 2006 in and near Terre Haute, Indiana. Three distinct study areas including two commercial classes, two residential classes, and two urban parks/recreational classes were selected for classification and analysis. Four commonly used classification methods – maximum likelihood (ML), extraction and classification of homogeneous objects (ECHO), spectral angle mapper (SAM), and iterative self organizing data analysis (ISODATA) - were applied to each data set. Accuracy assessment was conducted and overall accuracies were compared between the twenty four resulting thematic maps. With the exception of SAM and ISODATA in a complex commercial area, all methods employed classified the designated urban features with more than 80% accuracy. The thematic classification from ECHO showed the best agreement with ground reference samples. The residential area with relatively homogeneous composition was classified consistently with highest accuracy by all four of the classification methods used. The average accuracy amongst the classifiers was 93.60% for this area. When individually observed, the complex recreational area (Deming Park) was classified with the highest accuracy by ECHO, with an accuracy of 96.80% and 96.10% Kappa. The average accuracy amongst all the classifiers was 92.07%. The commercial area with relatively high complexity was classified with the least accuracy by all classifiers. The lowest accuracy was achieved by SAM at 63.90% with 59.20% Kappa. This was also the lowest accuracy in the entire analysis. This study demonstrates the potential for using the visible and near infrared (VNIR) bands from AISA+ hyperspectral data in urban LULC classification. Based on their performance, the need for further research using ECHO and SAM is underscored. The importance incorporating imaging spectrometer data in high resolution urban feature mapping is emphasized.
Neural Network Classification of Hyperspectral Imagery for Urban Environments: a Case StudyUrban environments are complex because many different artificial and natural objects occur in close proximity. Being able to understand the processes and workings of these environments requires the ability to observe and record data with high spatial and spectral resolution. Hyperspectral sensors have been gaining popularity for this task as they are becoming more affordable. In this research, a commonly used maximum likelihood (ML) classifier and artificial neural network (ANN) classifier have been compared for classifying urban land use and land cover (LULC) using AISA+ hyperspectral data. Further, the best set of bands were identified for classification of urban areas for use in ANN classification. Optimum bands based on a spectral separability measure were used with a neural network classifier to compare its performance with maximum likelihood classifier. It was found that both the classifiers had an overall classification accuracy of more than 80% and the neural network classifier with optimum band selection performed better in all of the study sites.