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: Detection and tracking algorithms, Target detection, Hyperspectral imaging, Spectroscopy, Image processing, Data modeling, Statistical analysis, Laser phosphor displays, Imaging systems, Signal to noise ratio
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: Statistical analysis, Hyperspectral imaging, Data modeling, Interference (communication), Error analysis, Principal component analysis, Signal to noise ratio, Data analysis, Vector spaces, Signal attenuation
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 ℓ<sub>2</sub>-norm denoted as ℓ<sub>2,∞-</sub>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.