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Generally, dimensionality reduction methods, such as Principle Component Analysis (PCA) and Negative Matrix Factorization (NMF), are always applied as the preprocessing part in hyperspectral image classification so as to classify the constituent elements of every pixel in the scene efficiently. The results, however, would suffer the loss of detailed information inevitably. In this paper, deep learning...
In hyperspectral images, there is abundant spectral information for classification. In most cases, spectral information based methods yield good classification results. However, different objects may have similar spectrums due to similar physical property. Spectral information based classification methods can't give accurate results for the similar physical property among objects belonging to the...
For the vast territory and the huge differences amongst cities in China, there is an urgent need to carry out research on the division of regional eco-types of cities, so that eco-city construction can be conducted to suit the local conditions. However, almost all the previous classification research is qualitative, such as experience summary and construction mode induction. In this paper, an ecological...
The insufficient number of training samples may often cause relatively low and unsteady accuracies in multi-sensor image classification. It is also difficult to properly deal with multi-source data simply by traditional classifiers. In this paper, we propose a novel active learning classification system to solve these problems. Firstly, the adaptive query by committee (AQBC) strategy could reduce...
In absence of prior knowledge of the pure signatures (endmembers) existing in a remotely sensed image which is often the case, the mean spectra of the pixel vectors directly extracted from the image scene are usually used in unmixing problems. This approach ignores some important statistical properties of the extracted samples, thus, leads to suboptimal solutions. This paper proposes a novel method...
High dimensionality of hyperspectral data and relatively limited training samples induce the Hughes phenomenon in hyperspectral image classification. To prevent this problem and decrease the computational cost, feature extraction often acts as pre-processing. In this paper, a subspace weighting kernel method combining clustering-based grouping is proposed for feature extraction in hyperspectral imagery...
The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire...
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