Abstract
Hyperspectral image (HSI) classification is an important research topic in detailed analysis of the Earth’s surface. However, the performance of the classification is often hampered by the high-dimensionality features and limited training samples of the HSIs which has fostered research about semi-supervised learning (SSL). In this paper, we propose a shape adaptive neighborhood information (SANI) based SSL (SANI-SSL) method that takes full advantage of the adaptive spatial information to select valuable unlabeled samples in order to improve the classification ability. The improvement of the classification mainly relies on two aspects: (1) the improvement of the feature discriminability, which is accomplished by exploiting spectral-spatial information, and (2) the improvement of the training samples’ representativeness which is accomplished by exploiting the SANI for both labeled and unlabeled samples. First, the SANI of labeled samples is extracted, and the breaking ties (BT) method is used in order to select valuable unlabeled samples from the labeled samples’ neighborhood. Second, the SANI of unlabeled samples are also used to find more valuable samples, with the classifier combination method being used as a strategy to ensure confidence and the adaptive interval method used as a strategy to ensure informativeness. The experimental comparison results tested on three benchmark HSI datasets have demonstrated the significantly superior performance of our proposed method.
Highlights
Hyperspectral remote sensing has been widely used in earth observation with the special advantages of obtaining rich spectral information with hundreds of narrow and continuous spectral bands [1,2]
Two new solutions have emerged in recent years [5]: one solution is to develop classifiers that can perform efficiently in the scenario of limited labeled samples and high-dimensional features, such as support vector machine (SVM) classifier [6,7] and multinomial logistic regression (MLR) [8,9]; the second solution is semi-supervised learning (SSL), in which unlabeled samples are introduced into the training sets in order to improve the capability of the classifier, because the unlabeled samples are helpful in improving the estimation of the class boundaries and can be obtained in a much easier way
We have presented a shape adaptive neighborhood information (SANI) based SSL method for Hyperspectral image (HSI) classification, which exploits the SANI of both labeled and unlabeled samples in order to make full use of the spectral-spatial information of the whole image
Summary
Hyperspectral remote sensing has been widely used in earth observation with the special advantages of obtaining rich spectral information with hundreds of narrow and continuous spectral bands [1,2]. When the number of labeled samples is limited, the so-called Hughes phenomenon often occurs, in that the classification accuracy decreases with increasing data dimensionality [4] To address this issue, two new solutions have emerged in recent years [5]: one solution is to develop classifiers that can perform efficiently in the scenario of limited labeled samples and high-dimensional features, such as support vector machine (SVM) classifier [6,7] and multinomial logistic regression (MLR) [8,9]; the second solution is semi-supervised learning (SSL), in which unlabeled samples are introduced into the training sets in order to improve the capability of the classifier, because the unlabeled samples are helpful in improving the estimation of the class boundaries and can be obtained in a much easier way
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