NDLI: Wavelet analysis and classification of urban environment using high-resolution multispectral image data

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Wavelet analysis and classification of urban environment using high-resolution multispectral image data

Content Provider	Semantic Scholar
Author	Myint, Soe Win
Copyright Year	2001
Abstract	Attempts to analyze urban features and classify land use and land cover directly from high-resolution satellite data with traditional computer classification techniques have proven to be inefficient. The fundamental problem usually found in identifying urban land cover types from high-resolution satellite imagery is that urban areas are composed of diverse materials (metal, glass, concrete, asphalt, plastic, trees, soil, etc.). These materials, each of which may have completely different spectral characteristics, are combined in complex ways by human beings. Hence, each urban land cover type may contain several different objects with different reflectance values. Noisy appearance with lots o f edges, and the complex nature of these images, inhibit accurate interpretation o f urban features. Traditional classifiers employ spectral information based on single pixel value and ignore a great amount of spatial information. Texture features play an important role in image segmentation and object recognition, as well as interpretation o f images in a variety o f applications ranging from medical imaging to remote sensing. This study analyzed urban texture features in multi-spectral image data. Recent development in the mathematical theory of wavelet transform has received overwhelming attention by the image analysts. An evaluation of the ability o f wavelet transform and other texture analysis algorithms in urban feature extraction and classification was performed in this study. Advanced Thermal Land Application Sensor (ATLAS) image data at 2.5 m spatial resolution acquired with 15 channel (0.45 pm 12.2 pm) were used for this research. The data were collected by a NASA Stennis LearJet 23 flying at 6600 feet over Baton Rouge, Louisiana, on May 7, 1999. The
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://digitalcommons.lsu.edu/cgi/viewcontent.cgi?article=1354&context=gradschool_disstheses
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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