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The potential of active learning (AL) methods for improving the marine oil spills identification system is exploited using 10-year(2004–2013) RADARSAT data. Six basic AL methods are proposed according to the uncertainty criteria and coupled with the support vector machine(SVM) classifier. As many as 56 commonly used features are used for the classification. The AUC measures are estimated using the...
This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and...
Classification has been among the central issues of hyperspectral application. However, due to the well-known Hughes phenomenon, most of the methods suffer from the curse of dimensionality and deeply rely on traditional dimensional reduction like Principle Component Analysis (PCA). In this paper, combining spatial and spectral information jointly, we propose a novel deep classification framework....
There are two important factors to improve the accuracy of the support vector machine(SVM) classifier. First, selected training samples should uniquely represent each class. Second, SVM training parameters which are pre-defined by the user should be suitable for training samples to obtain satisfied results of the SVM classifier. The proposed method of this paper presents a technique to adjust the...
In this paper, a decision fusion of pixel-level and superpixel-level classifiers (DFPSC) for the HSI is proposed. First, the support vector machine based classification probability combined with the local spatial information is introduced to classify the HSI in a pixel-by-pixel manner. Then, the HSI is over-segmented into non-overlapping superpixels. Each superpixel contains spatially-connected and...
The exploitation of the high revisit time (8–16 days) by the COSMO-SkyMed® (CSK®) satellites is an important opportunity for agricultural mapping. This study aims at evaluating CSK® potentiality to classify different crop types, with CSK® multi-temporal images collected over the agricultural site of Marchfeld, in Austria. Two different time series of CSK® HIMAGE SAR scenes, at 3m resolution, 9 at...
To combat the well-known Hughes phenomenon occurred in hyperspectral classification, most of the previous works adopt dimensionality reduction or manifold learning technique before supervised learning. While in this paper, we propose a different scheme: First, we design a pixel-wise classifier based on Convolutional Neural Network that could directly mapping observed spectrum to class distribution...
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