Fusion of Evidential CNN Classifiers for Image Classification

Hyperspectral Image Classification by Fusion of Multiple Classifiers

Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task. Secondly, due to lack of labeled training sample points, this paper proposes a new alg...

Cross-domain CNN for Hyperspectral Image Classification

In this paper, we address the dataset scarcity issue with the hyperspectral image classification. As only a few thousands of pixels are available for training, it is difficult to effectively learn high-capacity Convolutional Neural Networks (CNNs). To cope with this problem, we propose a novel cross-domain CNN containing the shared parameters which can co-learn across multiple hyperspectral dat...

application of image fusion (object fusion) for forest classification in northern forests of iran

forest classification on the basis of satellite images is a promising technique both for primary map production and for map updating and forest monitoring. for accurate for-est classification into three classes, using mapping by canopy cover density “high spatial resolution satellite images have to be used in order to obtain the required spatial detail” [schneider, 1999]. at the same time, the ...

An evidential Markovian model for data fusion and unsupervised image classification

Combining Classifiers for Improved Multilabel Image Classification

We propose a stacking-like method for multilabel image classification. Our approach combines the output of binary base learners, which use different features for image description, in a simple and straightforward way: The confidence values of the base learners are fed into a support vector machine (SVM) in order to improve prediction accuracy. Experiments on the datasets of the Pascal Visual Ob...