Domain adaptation is a proven hyperspectral image (HSI) classification approach aimed at transferring knowledge from a label-rich domain to a label-scarce domain. Existing literature assumes a closed-set scenario in which both the source and target domains share exactly the same label space (“known classes”). However, this assumption may be too ideal in practice. Often, the target domain contains private classes unknown to the source (“unknown classes”). It requires domain adaptation methods to classify the known classes accurately while simultaneously rejecting unknown classes. Focusing on the open-set setting, this paper creatively proposes a hyperspectral open set domain adaptation model based on adversarial learning with a three-dimensional convolutional neural network as the feature extractor, which can sufficiently explore joint spatial-spectral information of HSI and improve classification performance significantly. In addition, this model introduces a dynamic weighting scheme based on multiple auxiliary classifiers for inhibiting negative transfers during adversarial training. Experiment results on three benchmark hyperspectral datasets verify the superiority of the proposed approach for the hyperspectral open set classification. Compared with state-of-the-art techniques with and without using target samples during training, the proposed method improves the mean AUC values by at least 0.157, 0.028, and 0.163 on the Pavia University, Pavia Centre, and Indian Pines datasets, respectively.
Hyperspectral remote sensing technology has made good progress in recent years and is often used in military and civil fields. Hyperspectral images(HIS) are three-dimensional data composed of two dimensional spatial information and one dimensional spectral information of ground objects, which can be used to classify and study HIS. The current research is generally based on closed sets, that is, the classes appearing in the testing time all appear during the training time. However, the setting of this closed set is difficult to achieve in the real situation, so the concept of open sets is introduced, that is, unknown targets that may appear in the testing time but do not appear during the training time. However, there are few effective algorithms to study the open set of HIS. To solve this problem, we propose an open set recognition method for HIS based on Extreme Value Machine(EVM). The pre-processed HIS data was used as the input of EVM algorithm and the EVM model based on Weibull distribution was established. The test data were used to detect the classification of unknown targets and known targets. Compared with other classification algorithms, EVM can classify known targets in HIS and detect unknown targets with good accuracy.
KEYWORDS: Feature extraction, Hyperspectral imaging, Scene classification, Signal to noise ratio, Computer programming, Principal component analysis, Interference (communication), Unmanned aerial vehicles, Signal processing, GPU based image processing, Parallel computing
The classification of Hyperspectral images (HSIs) has been the focus of many recent research efforts, where feature extraction plays an important role. Discriminative feature extraction methods aim to reduce the data dimension of HSIs, retain effective image information to the greatest extent, and suppress noises at the same time. Besides, according to the characteristics of pixel-by-pixel-multi-band of HSIs and data redundancy between bands, the processing of HSIs in the classifier will bring huge computational overhead. In this paper, we present a parallel implementation of the improved noise adaptive principal component algorithm (INAPC) for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). Aiming at maximizing the signal-to-noise ratio (SNR) instead of the variance, we firstly deploy two SVDs and more comprehensive noise estimation in the INAPC transform and constructed a complete feature extraction process. Then we deploy a complete CPU-GPU collaborative computing solution, and use several GPU programming optimization methods to achieve the maximum acceleration effect. Through the experiments on three real hyperspectral datasets, Experimental results show that the proposed INAPC has stable superiority and provides a significant speedup compared to the CPU implementation.
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