Abstract
Active Learning (AL) for Hyperspectral Image Classification (HSIC) has been extensively studied. However, the traditional AL methods do not consider randomness among the existing and new samples. Secondly, very limited AL research has been carried out on joint spectral–spatial information. Thirdly, a minor but still worth mentioning factor is the stopping criteria. Therefore, this study caters to all these issues using a spatial prior Fuzziness concept coupled with Multinomial Logistic Regression via a Splitting and Augmented Lagrangian (MLR-LORSAL) classifier with dual stopping criteria. This work further compares several sample selection methods with the diverse nature of classifiers i.e., probabilistic and non-probabilistic. The sample selection methods include Breaking Ties (BT), Mutual Information (MI) and Modified Breaking Ties (MBT). The comparative classifiers include Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbour (KNN) and Ensemble Learning (EL). The experimental results on three benchmark hyperspectral datasets reveal that the proposed pipeline significantly increases the classification accuracy and generalization performance. To further validate the performance, several statistical tests are also considered such as Precision, Recall and F1-Score.
Highlights
Hyperspectral imaging (HSI) examines the reflection of light of an object across a wide range of electromagnetic spectra instead of just associating primary colors to a pixel [1]
One can conclude that our proposed approach significantly enhances the classification performance with comparatively less computational time
In all the above-shown experiments, the performance of the proposed pipeline is being evaluated using a set of experiments i.e., first, we analyze several sample selection methods for MLR-LORSAL
Summary
Hyperspectral imaging (HSI) examines the reflection of light of an object across a wide range of electromagnetic spectra instead of just associating primary colors to a pixel [1]. The major limitation of supervised HSI classification is the poor performance due to the Hughes phenomena [24] It occurs when the ratio of spectral bands is significantly less as compared to the labeled training samples available in hyperspectral data [25]. To cope with the aforesaid issues, one of the commonly used semi-supervised approaches is the expansion of the initial training set by efficiently utilizing unlabeled data This method is known as Active Learning (AL) which significantly improves the performance of classification techniques by adding new examples in the training set for the cycle of training, unless a stopping criteria is met, i.e., required classification accuracy. The motivation of our current work is to investigate several state-of-the-art sample selection and classification techniques with predefined dual stopping criteria and to properly generalize them for remotely sensed hyperspectral datasets.
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