A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme.

The Reed-Xiaoli (RX) algorithm has been widely used as an anomaly detector for hyperspectral images. Recently, kernel RX (KRX) has been proven to yield high performance in anomaly detection and change detection. In this paper, we present a generalization of the KRX algorithm. The novel algorithm is called cluster KRX (CKRX), which becomes KRX under certain conditions. The key idea is to group background pixels into clusters and then apply a fast eigendecomposition algorithm to generate the anomaly detection index. Both global and local versions of CKRX have been implemented. Application to anomaly detection using actual hyperspectral images is included. In addition to anomaly detection, the CKRX algorithm has been integrated with other prediction algorithms for change detection. Spatially registered visible and near-infrared hyperspectral images collected from a tower-based geometry have been used in the anomaly and change detection studies. Receiver operating characteristics curves and actual computation times were used to compare different algorithms. It was demonstrated that CKRX has comparable detection performance as KRX, but with much lower computational requirements.

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.

Anomaly detection has been known to be a challenging problem due to the uncertainty of anomaly and the interference of noise. In this paper, we focus on anomaly detection in hyperspectral images (HSIs) and propose a novel detection algorithm based on spectral unmixing and dictionary-based low-rank decomposition. The innovation is threefold. First, due to the highly mixed nature of pixels in HSI data, instead of using the raw pixel directly for anomaly detection, the proposed algorithm applies spectral unmixing to obtain the abundance vectors and uses these vectors for anomaly detection. We show that the abundance vectors possess more distinctive features to identify anomaly from background. Second, to better represent the highly correlated background and the sparse anomaly, we construct a dictionary based on the mean shift clustering of the abundance vectors to improve both the discriminative and representative powers of the algorithm. Finally, a low-rank matrix decomposition method based on the constructed dictionary is proposed to encourage the coefficients of the dictionary, instead of the background itself, to be low rank, and the residual matrix to be sparse. Anomalies can then be extracted by summing up the columns of the residual matrix. The proposed algorithm is evaluated on both synthetic and real data sets. Experimental results show that the proposed approach constantly achieves high detection rate, while maintaining low false alarm rate regardless of the type of images tested.