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Although hyperspectral anomaly detection is commonly conducted in the visible, near-infrared, and shortwave infrared spectral regions, there has been less research on hyperspectral anomaly detection in the longwave infrared (LWIR) hyperspectral region. The radiance of thermal infrared hyperspectral imagery is determined by the temperature and emissivity. To avoid the detection uncertainty caused by the single factor of temperature, emissivity can be introduced to detect anomalies. However, in the emissivity domain, the spectral contrast and signal-to-noise ratio (SNR) are low, which makes it difficult to separate the anomalies from the background. In this paper, an anomaly detection method combining emissivity and a segmented low-rank prior (EaSLRP) is proposed for use with thermal infrared hyperspectral imagery. The EaSLRP method is divided into three parts—1) temperature/emissivity retrieval, 2) extraction of the thermal infrared hyperspectral background information, and 3) Mahalanobis distance detection. A homogeneous region generation method is also proposed to solve the problem of the complex global background leading to inaccurate background estimation. The GoDec method is used for matrix decomposition and background information extraction and to remove some of the noise. The proposed Mahalanobis distance detector then uses the background component and original image for anomaly detection, while highlighting the spectral difference between the anomalies and background. This method can also suppress the influence of noise, to some extent. The experimental results obtained with airborne Fourier transform thermal infrared spectrometer hyperspectral images demonstrate that the EaSLRP method is effective when compared with the Reed–Xiaoli detector (RXD), the segmented RX detector (SegRX), the low-rank and sparse representation-based detector (LRASR), the low-rank and sparse matrix decomposition (LRaSMD)-based Mahalanobis distance method (LSMAD), and the locally enhanced low-rank prior method (LELRP-AD).

The generation and evolution of artificial plasma clouds is a complicated process that is strongly dependent on the background environment and release conditions. In this paper, based on a three-dimensional two-species fluid model, the evolution characteristics of artificial plasma clouds under various release conditions were analyzed numerically. In particular, the effect of ionospheric density gradient and ambient horizontal wind field was taken into account in our simulation. The results show that an asymmetric plasma cloud structure occurs in the vertical direction when a nonuniform ionosphere is assumed. The density, volume, and expansion velocity of the artificial plasma cloud vary with the release altitude, mass, and initial ionization rate. The initial release velocity can change the cloud's movement and overall distribution. With an initial velocity perpendicular to the magnetic field, an O+ density cavity and two bumps exist. When there is an initial velocity parallel to the magnetic field, the generated plasma cloud is bulb-shaped, and only one O+ density cavity and one density bump are created. Compared to the cesium case, barium clouds expand more rapidly. Moreover, Cs+ clouds have a higher density than Ba+ clouds, and the snowplow effect of Cs+ is also stronger.

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