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This paper presents a new spatial-spectral classification method for hyperspectral images, which consists of three main techniques. Firstly, fully constrained least squares (FCLS) that is common in hyperspectral unmixing is investigated for hyperspectral image classification in kernel Hilbert space. Secondly, the spatial-spectral information of hyperspectral images is exploited to improve the classification...
This paper presents a region-based relaxed multiple kernel collaborative representation method for the spatial-spectral classification of hyperspectral images. The proposed method consists of three steps. In the first step, a multiscale method achieved by extending a superpixel segmentation algorithm is designed to capture the spatial-spectral information of hyperspectral images. For each scale, a...
This paper considers geolocating a target with known altitude using azimuth angle measurements obtained at multiple spatially separated ground sensors. Under Gaussian noise model, the target position Cramér-Rao lower bound (CRL-B) is derived and the geolocation problem is formulated as an inequality-constrained weighted least squares (WLS) optimization problem. A new two-step algorithm that mainly...
In this paper, we address the problem of temporal alignment of surfaces for subjects dressed in wide clothing, as acquired by calibrated multi-camera systems. Most existing methods solve the alignment by fitting a single surface template to each instant's 3D observations, relying on a dense point-to-point correspondence scheme, e.g. by matching individual surface points based on local geometric features...
The probability hypothesis density (PHD) filter is a promising tool for tracking the time-varying number of targets in real time. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. By using Gaussian component labels in GM-PHD filter, the identities of individual target can be obtained. However, the labeling GM-PHD filter cannot correctly...
Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has proven to be a promising algorithm for multiple extended target tracking with shape estimation. However, as far as I know, this method only can be used in the single sensor tracking system, which cannot obtain the accurate state estimates for the complex tracking scenario. To solve this problem, we propose a multi-sensor...
Extended target probability hypothesis density filter based on the Gaussian mixture technique, referred to as the ET-GM-PHD algorithm, has proved to be a promising algorithm for multiple extended target tracking. However, this method can only be used in the multi-target tracking systems with a known measurement rate. Otherwise, the tracking performance will decline greatly by using error value of...
Probability hypothesis density (PHD) filter is an optimal Bayesian multi-target filter based on random finite set. Gaussian mixture is an approximation scheme of PHD filter, which is suitable for linear Gaussian case. In multi-target tracking, when targets are moving closely to each other, GM-PHD filter cannot correctly estimate the number of targets and target states in complex tracking environment...
For the multiple extended target tracking (METT), one crucial problem is how to partition the measurement sets accurately and rapidly. Due to the disturbance of clutter, the conventional methods, such as distance partition method, K-means++ method, etc., cannot give a perfect partition. In this paper, a novel partition method is proposed based on density analysis and spectral clustering technique...
We present four human behavioral experiments to address the question of intuitive granularities in fundamental spatial relations as they can be found in formal spatial calculi that focus on invariant characteristics under certain (especially topological) transformations. Of particular interest to this article is the concept of two spatially extended entities overlapping each other. The overlap concept...
When tracking maneuvering targets, the sudden changes of target states and nonlinear problems in passive tracking cause serious decline and even divergence in the performance of some conventional algorithms. Taking this into account, a novel adaptive tracking algorithm for maneuvering targets is proposed in this paper. First, a new weighed factor could adaptively adjust the filter gain is introduced...
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