Developing a Spectral-Based Strategy for Urban Object Detection From Airborne Hyperspectral TIR and Visible Data

PROJECT TITLE :

Developing a Spectral-Based Strategy for Urban Object Detection From Airborne Hyperspectral TIR and Visible Data

ABSTRACT:

Classification and detection of urban objects are massive challenges for years. High spatial resolution hyperspectral thermal infrared (HSR-HTIR) is a novel supply of information that became available in recent times for urban object detection. During this research, a unique method is proposed for integration of HTIR and very high spatial resolution (VHSR) visible image to classify urban objects. First, atmospheric corrections were enforced to the HSR-HTIR. Second, for the first time, projection pursuit (PP) band reduction method was applied to a completely unique source of knowledge, and the results achieved are better than those obtained by applying principal element analysis (PCA) as a well known band reduction approach. Then, numerous options derived from HSR-HTIR and VHSR images were fed to a pixel-based mostly support vector machine (SVM) classification algorithm, and 7 urban classes detected. Afterward, an innovative strategy, using object-rule-based mostly postprocessing approach, was introduced for postclassification of the raw classification results. Finally, a call-based mostly overlaying method was administered to produce the ultimate map. The classification results obtained indicate the high potential of using solely spectral options. Consequently, its implementation becomes more possible and therefore the accuracies obtained are competitive in comparison to the results announced previously by the IEEE Geoscience and Remote Sensing Society (GRSS) Information Fusion contest 2014.

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