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This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the featur...
A kernel-based method for very high spatial resolution remote sensing image classification is proposed in this article. The new kernel method is based on spectral-spatial information and structure inf...
In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume ...
In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped f...
With the increasing demand for the digital survey and acquisition of road pavement conditions, there is also the parallel growing need for the development of automated techniques for the analysis and ...
This paper proposes a distance-based kernel change detection algorithm (DKCD). The input vectors from two images of different times are mapped into a potentially much higher dimensional feature space...
Support Vector Machines (SVMs) are a statistical learning theory based techniques and have been applied to different fields. For the pattern recognition case, SVMs have been used for isolated handwri...
Agriculture holds a pivotal role in context to India, which is basically agrarian economy. Crop type identification is a key issue for monitoring agriculture and is the basis for crop acreage and yiel...
In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imagerie...
A framework for multitemporal change detection based on kernel-composition is applied to a multispectral-multitemporal classification scenario, evaluated and compared to traditional change detection a...
This thesis analyzes the anomalous measurement metric in high dimension feature space, where it is supposed the Gaussian assumption for state-of-art mahanlanobis algorithms is reasonable. The realizat...

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