Abstract
Hyperspectral image (HSI) classification has been long envisioned in the remote sensing community. Many methods have been proposed for HSI classification. Among them, the method of fusing spatial features has been widely used and achieved good performance. Aiming at the problem of spatial feature extraction in spectral-spatial HSI classification, we proposed a guided filter-based method. We attempted two fusion methods for spectral and spatial features. In order to optimize the classification results, we also adopted a guided filter to obtain better results. We apply the support vector machine (SVM) to classify the HSI. Experiments show that our proposed methods can obtain very competitive results than compared methods on all the three popular datasets. More importantly, our methods are fast and easy to implement.
Highlights
Hyperspectral imaging sensors have been widely used in remote sensing, biology, chemometrics, and so on [1]
3.1 The proposed Guided Filter support vector machine (SVM) Edge Preserving Filter (GF-SVM-EPF) We propose a novel method for hyperspectral image (HSI) classification with SVM and guided filter
The result obtained by Connected SVM (Co-SVM) which employs the fusion of guided information and spectral information is worse than that obtained by GF-SVM which only employs the guided information
Summary
Hyperspectral imaging sensors have been widely used in remote sensing, biology, chemometrics, and so on [1]. The task of classification is to assign a unique label to each pixel vector of HSI For this problem, many pixel-wise (spectral-based) methods were employed, including k-nearest neighbors (KNN) [5], support vector machine (SVM) [6], and sparse representation [7] in the last two decades. For SVM methods, the mainstream approaches of fusing spectral and spatial features are used by kernel combination [14]. Kang [18] proposed a spectral-spatial HSI classification method with edge-preserving filtering, which extracted the spatial features after the SVM classification and got competitive results. 1) We adopt the guided filter to smooth HSI, which is similar to de-noising in image processing By this method, a fusion which consists of a pixel and its neighboring pixel information is generated.
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