In this paper, the noncentral chi-squared distribution is applied in the Constant False Alarm Rate (CFAR) detection of hyperspectral projected images to distinguish the anomaly points from background. Usually, the process of the hyperspectral anomaly detectors can be considered as a linear projection. These operators are linear transforms and their results are quadratic form which comes from the transform of spectral vector. In general, chi-squared distribution could be the proper choice to describe the statistical characteristic of this projected image. However, because of the strong correlation among the bands, the standard central chi-squared distribution often cannot fit the stochastic characteristic of the projected images precisely. In this paper, we use a noncentral chi-squared distribution to approximate the projected image of subspace based anomaly detectors. Firstly, the statistical modal of the projected multivariate data is analysed, and a noncentral chi-squared distribution is deduced. Then, the approach of the parameters calculation is introduced. At last, the aerial hyperspectral images are used to verify the effectiveness of the proposed method in tightly modeling the projected image statistic distribution.
In this paper, a novel local ways to implement hyperspectral anomaly detector is presented. Usually, the local detectors
are implemented in the spatial window of image scene, but the proposed approach is implemented on the windows of
spectral space. As a multivariate data, the hyperspectral image datasets can be considered as a low-dimensional manifold
embedded in the high-dimensional spectral space. In real environments, nonlinear spectral mixture occurs more
frequently. At these situations, whole dataset would be distributed in one or more nonlinear manifolds in high
dimensional space, such as a hyper-curve surface or nonlinear hyper-simplex. However, the majority of global and local
detectors in hyperspectral image are based on the linear projections. They are established on the assumption that the
geometric distribution of datasets is a linear manifold. It is incapable for them to deal with these nonlinear manifold data,
even for spatial local data. In this paper, a novel anomaly detection algorithm based on local linear manifold is put
forward to handle the nonlinear manifold problems. In the algorithm, the local neighborhood relationships are
established in spectral space, and then an anomaly detector based on linear projection is carried out in these local areas.
This situation is similar to using sliding windows in the spectral space. The results are compared with classic spatial
local algorithm by using real hyperspectral image and demonstrate the effectiveness in improving the weak anomalies
detection and decreasing the false alarms.
Proc. SPIE. 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
KEYWORDS: Target detection, Signal to noise ratio, Hyperspectral imaging, Statistical analysis, Detection and tracking algorithms, Data modeling, Imaging systems, Image processing, Spectroscopy, Laser phosphor displays
In this paper, a new approach of anomaly detection based on low dimensional manifold will be elaborated. Hyperspectral image data set is considered as a low-dimensional manifold embedded in the high-dimensional spectral space, and this manifold has special geometrical structure, such as Hyper-plane. Usually, the main body of this manifold is constituted by a large area of background spectrum while the anomalistic objects are outside of the manifold. Through the analysis of the geometrical characteristics and the calculation of the appropriate projection direction, anomalistic objects can be separated from background effectively, so as to achieve the purpose of anomaly detection. Experimental results obtained from both the ground and airborne spectrometer data prove effectiveness of the algorithm in improving the detection performance. Since there are no available prior target spectrums to provide proper projected direction, the weak anomalies which have subtle differences from the background on the spectrum will be undetected.
Proc. SPIE. 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications
KEYWORDS: Signal to noise ratio, Hyperspectral imaging, Principal component analysis, Statistical analysis, Data modeling, Signal attenuation, Error analysis, Interference (communication), Vector spaces, Data analysis
Dimensionality Reduction (DR) for hyperspectral image data can be regarded as a problem of signal subspace estimation (SSE) in terms of the Linear Mixing Model (LMM). Most SSE methods for hyperspectral data are based on the analysis of second-order statistics (SOS) without considering preservation of anomalies. This paper addresses the problem of SSE for preserving both abundant and rare signal components in hyperspectral images. The multivariate sample skewness for testing normality is brought in our new algorithm as a discrimination index for rank determination of rare vectors subspace, combining with analysis of the maximum of data-residual ℓ2-norm denoted as ℓ2,∞-norm which is strongly influenced by the anomaly signal components. And the SOS based method, labeled as hyperspectral signal subspace identification by minimum error (HySime), is employed for identification of abundant vectors space. The results of experiments on real AVIRIS data prove that multivariate sample skewness statistics is suitable for measuring the distribution about hyperspectral data globally, and our algorithm can obtain the anomaly components from data that are discarded by HySime, which implies less information loss in the our method.
There are two problems when associating multiple targets in remote sensing images: Firstly, with low temporal
resolution observation, the target's kinematic state cannot be estimated accurately and the classical Kalman filtering
association algorithms are no more applicable. Secondly, the classical image feature-based target matching algorithms
cannot deal with the illegibility of multiple targets' correspondence, which don't take into account the uncertainty of
feature extraction. To resolve above problems, a novel multiple targets association method based on Multi-scale
Autoconvolution(MSA) features matching and global association cost optimization through simulated annealing (SA)
algorithm is proposed. Experiments with remote sensing images show the applicability of the method for multiple targets