Hyperspectral images have the capability of acquiring images of earth surface with several hundred of spectral bands. Providing such abundant spectral data should increase the abilities in classifying land use/cover type. However, due to the high dimensionality of hyperspectral data, traditional classification methods are not suitable for hyperspectral data classification. The common method to solve this problem is dimensionality reduction by using feature extraction before classification. Kernel methods such as support vector machine (SVM) and multiple kernel learning (MKL) have been successfully applied to hyperspectral images classification. In kernel methods applications, the selection of kernel function plays an important role. The wavelet kernel with multidimensional wavelet functions can find the optimal approximation of data in feature space for classification. The SVM with wavelet kernels (called WSVM) have been also applied to hyperspectral data and improve classification accuracy. In this study, wavelet kernel method combined multiple kernel learning algorithm and wavelet kernels was proposed for hyperspectral image classification. After the appropriate selection of a linear combination of kernel functions, the hyperspectral data will be transformed to the wavelet feature space, which should have the optimal data distribution for kernel learning and classification. Finally, the proposed methods were compared with the existing methods. A real hyperspectral data set was used to analyze the performance of wavelet kernel method. According to the results the proposed wavelet kernel methods in this study have well performance, and would be an appropriate tool for hyperspectral image classification.
KEYWORDS: Landslide (networking), Data mining, Calibration, Data modeling, Hazard analysis, Data analysis, Knowledge discovery, Geographic information systems, Remote sensing, Meteorology
Due to the particular geographical location and geological condition, Taiwan suffers from many natural hazards which
often cause series property damages and life losses. To reduce the damages and casualty, an effective real-time system
for hazard prediction and mitigation is necessary. In this study, a case study for Landslide Hazard Zonation (LHZ) is
tested in accordance with Spatial Data Mining and Knowledge Discovery (SDMKD) from database. Many different
kinds of geospatial data, such as the terrain elevation, land cover types, the distance to roads and rivers, geology maps,
NDVI, and monitoring rainfall data etc., are collected into the database for SDMKD. In order to guarantee the data
quality, the spatial data cleaning is essential to remove the noises, errors, outliers, and inconsistency hiding in the input
spatial data sets. In this paper, the Kriging interpolation is used to calibrate the QPESUMS rainfall data to the rainfall
observations from rain gauge stations to remove the data inconsistency. After the data cleaning, the artificial neural
networks (ANNs) is applied to generate the LHZ map throughout the test area. The experiment results show that the
accuracy of LHZ is about 92.3% with the ANNs analysis, and the landslides induced by heavy-rainfall can be mapped
efficiently from remotely sensed images and geospatial data using SDMKD technologies.
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