The Convolutional Neural Network (CNN) has promoted Hyperspectral Images (HSIs) classification performance. However, the size of the convolutional kernel is fixed, whereas the size of objects in HSIs varies greatly; training a CNN requires a large number of samples with label, but manually tagging each pixel of HSIs is time-consuming and labor-intensive. To address above problems, a Semi-supervised Robust Self-ensemble Network (SRSN) is proposed in this letter. The SRSN contains a basic network and an ensemble network. The two networks can learn from each other to realize self-ensemble learning. Specifically, the deformable convolution, which is originally applied to the spatial dimension, is extended to the spectral dimension, thereby effectively solves the problem of CNN’s fixed convolutional kernel. Concurrently, to enhance the performance of the semi-supervised classifier, a consistency filter is proposed to screen unlabeled samples with high confidence. Experiments were carried out on the international common test datasets. The experimental results fully prove that the SRSN model proposed in this letter is superior to other methods and achieves 97.28%, 82.88% and 89.13% OA of PaviaCenter, Houston2013 and WHU-Hi-HongHu datasets.

Generally, multimodality data contain different potential information available and are capable of providing an enhanced analytical result compared to monosource data. The way to combine the data plays a crucial role in multimodality data analysis which is worth investigating. Multimodality clustering, which seeks a partition of the data in multiple views, has attracted considerable attention, for example, robust multiview spectral clustering (RMSC) explicitly handles the possible noise in the transition probability matrices associated with different views. Spectral clustering algorithm embeds the input data into a low-dimensional representation by dividing the clustering problem into k subproblems, and the corresponding eigenvalue reflects the loss of each subproblem. So, the eigenvalues of the Laplacian matrix should be treated differently, while RMSC regularizes each singular value equally when recovering the low-rank matrix. In this paper, we propose a multimodality clustering algorithm which recovers the low-rank matrix by weighted nuclear norm minimization. We also propose a method to evaluate the weight vector by learning a shared low-rank matrix. In our experiments, we use several real-world datasets to test our method, and experimental results show that the proposed method has a better performance than other baselines.

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.