A Positive Boolean Functions Classifer Based on Three-dimension Simulated Annealing Band Selection Approach to Hyperspectral Images

• Main Authors: Jia-Chi Lia, 賴嘉琪
• Other Authors: Wen-Yew Liang
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/xevz48

碩士 === 國立臺北科技大學 === 通訊與資訊產業研發碩士專班 === 96 === A sophisticated remote sensing technique was introduced for decades to display and realize various topographical features, such as the distribution of mountains, plains, basins, coastlines, cities, rivers and roads, over a large area of terrain. Moreover, a more detailed topographic distribution, such as the direction of the mountain, river flows, geometry morphology, can also be examined by this modern satellite technology. In telemetry images science, the technologies of satellite sensors are increasingly developed, resulting in the huge availability of spectrum; however, minor noise and erroneous information are also detected. Therefore the selection of the correct and effective spectral information to be classified, and pre-processing of the characteristics sampling are apparently significant. Thesis adopts the simulated annealing method of three-dimensional (3D) feature extraction technology to re-classify the Hyperspectral feature extraction by four classifiers: Positive Boolean Function (PBF), Maximum Likelihood (ML), Support Vector Machine (SVM), and k Nearest Neighborhood classifiers (kNN). PBF classifier improves the sampling ratio, and strengthens the correction. ML classifier is generated based on the probability; SVM classifier uses the core function to implement learning training. kNN classifier utilizes the distance between examples and itself to classify. This thesis proposes a 3D simulated annealing (3DSA) feature extraction approach for the band selections prior to the PBF classifications. Based on the experimental results, the proposed 3DSA/PBF method presents a higher accuracy compared to other classifiers. It can also be an alternative of the satellite remote sensing image classifications.