Detecting a target with low-occurrence-probability from unknown background in a hyperspectral image, namely anomaly detection, is of practical significance. Reed-Xiaoli (RX) algorithm is considered as a classic anomaly detector, which calculates the Mahalanobis distance between local background and the pixel under test. Local RX, as an adaptive RX detector, employs a dual-window strategy to consider pixels within the frame between inner and outer windows as local background. However, the detector is sensitive if such a local region contains anomalous pixels (i.e., outliers). In this paper, a locality-constrained anomaly detector is proposed to remove outliers in the local background region before employing the RX algorithm. Specifically, a local linear representation is designed to exploit the internal relationship between linearly correlated pixels in the local background region and the pixel under test and its neighbors. Experimental results demonstrate that the proposed detector improves the original local RX algorithm.
Sparse representation-based classification (SRC) has gained great interest recently.
A pixel to be classified is sparsely approximately by labeled samples, and it is assigned to the
class whose labeled samples provide the smallest representation error. In this paper, we extend
SRC by exploiting the benefits of using a smoothing filter based on sparse gradient
minimization. The smoothing filter is expected to provide less intra class variability and more
spatial regularity, which eliminating the inherent variations within a small neighborhood.
Classification performance on two real hyperspectral datasets demonstrates that our proposed
method has improved classification accuracy and the resulting accuracies are persistently
higher at all small training sample size situations compared to some traditional classifiers.
This paper presents a dynamic classifier selection approach for hyperspectral image classification, in which both spatial and spectral information are used to determine a pixel’s label once the remaining classified pixels’ neighborhood meets the threshold. For volumetric texture feature extraction, a volumetric gray level co-occurrence matrix is used; for spectral feature extraction, a minimum estimated abundance covariance-based band selection is used. Two hyperspectral remote sensing datasets, HYDICE Washington DC Mall and AVIRIS Indian Pines, are employed to evaluate the performance of the developed method. The classification accuracies of the two datasets are improved by 1.13% and 4.47%, respectively, compared with the traditional algorithms using spectral information. The experimental results demonstrate that the integration of spectral information with volumetric textural features can improve the classification performance for hyperspectral images.
This paper presents a comparison of two real-time hand gesture recognition systems. One system utilizes a binocular stereo camera set-up while the other system utilizes a combination of a depth camera and an inertial sensor. The latter system is a dual-modality system as it utilizes two different types of sensors. These systems have been previously developed in the Signal and Image Processing Laboratory at the University of Texas at Dallas and the details of the algorithms deployed in these systems are reported in previous papers. In this paper, a comparison is carried out between these two real-time systems in order to examine which system performs better for the same set of hand gestures under realistic conditions.
A wavelet-based nearest-regularized-subspace classifier is proposed for noise-robust
hyperspectral image (HSI) classification. The nearest-regularized subspace, coupling the nearest-
subspace classification with a distance-weighted Tikhonov regularization, was designed to
only consider the original spectral bands. Recent research found that the multiscale wavelet features
[e.g., extracted by redundant discrete wavelet transformation (RDWT)] of each hyperspectral
pixel are potentially very useful and less sensitive to noise. An integration of wavelet-based
features and the nearest-regularized-subspace classifier to improve the classification performance
in noisy environments is proposed. Specifically, wealthy noise-robust features provided
by RDWT based on hyperspectral spectrum are employed in a decision-fusion system or as
preprocessing for the nearest-regularized-subspace (NRS) classifier. Improved performance
of the proposed method over the conventional approaches, such as support vector machine,
is shown by testing several HSIs. For example, the NRS classifier performed with an accuracy
of 65.38% for the AVIRIS Indian Pines data with 75 training samples per class under noisy
conditions (signal-to-noise ratio ¼ 36.87 dB), while the wavelet-based classifier can obtain
an accuracy of 71.60%, resulting in an improvement of approximately 6%.