Evaluation of Import Vector Machines for Classifying Hyperspectral Data
نویسندگان
چکیده
Zusammenfassung We evaluate the performance of Import Vector Machines (IVM), a sparse Kernel Logistic Regression approach, for the classification of hyperspectral data. The IVM classifier is applied on two different data sets, using different number of training samples. The performance of IVM to Support Vector Machines (SVM) is compared in terms of accuracy and sparsity. Moreover, the impact of the training sample set on the accuracy and stability of IVM was investigated. The results underline that the IVM perform similar when compared to the popular SVM in terms of accuracy. Moreover, the number of import vectors from the IVM is significantly lower when compared to the number of support vectors from the SVM. Thus, the classification process of the IVM is faster. These findings are independent from the study site, the number of training samples and specific classes. Consequently, the proposed IVM approach is a promising classification method for hyperspectral imagery.
منابع مشابه
High performance of the support vector machine in classifying hyperspectral data using a limited dataset
To prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. Due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the Hughes phenomenon. A practical way to handle the Hughes problem is preparing a lot of training samples until the size ...
متن کاملA classifier ensemble based on fusion of support vector machines for classifying hyperspectral data
Classification of hyperspectral data using a classifier ensemble that is based on support vector machines (SVMs) are addressed. First, the hyperspectral data set is decomposed into a few data sources according to the similarity of the spectral bands. Then, each source is processed separately by performing classification based on SVM. Finally, all outputs are used as input for final decision fus...
متن کاملDetection of Disease Symptoms on Hyperspectral 3d Plant Models
We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of in...
متن کاملMulti-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines
Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extreme...
متن کاملRoof Surface Classification with Hyperspectral and Laser Scanning Data – An Assessment of Spectral Angle Mapper and Support Vector Machines
The urban environment is characterised by a variety of different surface materials. For the discrimination of urban materials, hyperspectral imaging proved a valuable tool. In this study, two methods for classification, Spectral Angle Mapper and Support Vector Machines, are compared on a hyperspectral dataset to derive a detailed map of roof materials. Spectral similarity of different materials...
متن کامل