Infrared (IR) small target detection with high detection rate, low false alarm rate, and high detection speed has a significant value, but it is usually very difficult since the small targets are usually very dim and may be easily drowned in different types of interferences. Current algorithms cannot effectively enhance real targets and suppress all the types of interferences simultaneously. In this letter, a multiscale detection algorithm utilizing the relative local contrast measure (RLCM) is proposed. It has a simple structure: first, the multiscale RLCM is calculated for each pixel of the raw IR image to enhance real targets and suppress all the types of interferences simultaneously; then, an adaptive threshold is applied to extract real targets. Experimental results show that the proposed algorithm can deal with different sizes of small targets under complex backgrounds and has a better effectiveness and robustness against existing algorithms. Besides, the proposed algorithm has the potential of parallel processing, which is very useful for improving the detection speed.

In uncooled long-wave IR camera systems, the temperature of a focal plane array (FPA) is variable along with the environmental temperature as well as the operating time. The spatial nonuniformity of the FPA, which is partly affected by the FPA temperature, obviously changes as well, resulting in reduced image quality. This study presents a real-time nonuniformity correction algorithm based on FPA temperature to compensate for nonuniformity caused by FPA temperature fluctuation. First, gain coefficients are calculated using a two-point correction technique. Then offset parameters at different FPA temperatures are obtained and stored in tables. When the camera operates, the offset tables are called to update the current offset parameters via a temperature-dependent interpolation. Finally, the gain coefficients and offset parameters are used to correct the output of the IR camera in real time. The proposed algorithm is evaluated and compared with two representative shutterless algorithms [minimizing the sum of the squares of errors algorithm (MSSE), template-based solution algorithm (TBS)] using IR images captured by a $384\times 288$ pixel uncooled IR camera with a 17 μm pitch. Experimental results show that this method can quickly trace the response drift of the detector units when the FPA temperature changes. The quality of the proposed algorithm is as good as MSSE, while the processing time is as short as TBS, which means the proposed algorithm is good for real-time control and at the same time has a high correction effect.

The relation between spontaneous Rayleigh Brillouin (SRB) spectrum linewidth, gas temperature, and pressure are analyzed at the temperature range from 220 to 340 K and the pressure range from 0.1 to 1 bar, covering the stratosphere and troposphere relevant for the Earth’s atmosphere and for atmospheric Lidar missions. Based on the analysis, a model retrieving gas temperature from directly measured linewidth is established and the accuracy limitations are estimated. Furthermore, some experimental data of air and nitrogen are used to verify the accuracy of the model. As the results show, the retrieved temperature shows good agreement with the reference temperature, and the absolute difference is less than 3 K, which indicates that this method provides a fruitful tool in satellite retrieval to extract the gaseous properties of atmospheres on-line by directly measuring the SRB spectrum linewidth.

Local contrast methods are efficient approaches for infrared (IR) small target detection. However, since its baseline is obtained locally, a local contrast method can work well only when the target is locally prominent, which is not always the case when the background is very complex. In this paper, a new detection framework named true global contrast measure (TGCM) is proposed, in which its baseline is obtained by global decomposition, so that it can handle situations where the target is not locally salient. For example, when it is adjacent to some bright background. The complex edges / corners and noises that are easily lost during decomposition will be suppressed through some simple but ingenious design: A Gaussian filtering operation is adopted to suppress noises, and a local max dilation is utilized on the separated baseline to maintain as much edge / corner information as possible. The residual information during decomposition will be used as a weighting function to further suppress clutters. Experiments on both real and simulated IR images demonstrated the effectiveness of the proposed method. Compared to some existing algorithms, it has advantages in target enhancement and background suppression. Especially, it can achieve good performance in certain scenarios where the target is not locally prominent.

A lidar system with Fabry–Pérot etalon and an intensified charge coupled device can be used to obtain the scattering spectrum of the ocean and retrieve oceanic temperature profiles. However, the spectrum would be polluted by noise and result in a measurement error. To analyze the effect of signal to noise ratio (SNR) on the accuracy of measurements for Brillouin lidar in water, the theory model and characteristics of SNR are researched. The noise spectrums with different SNR are repetitiously measured based on simulation and experiment. The results show that accuracy is related to SNR, and considering the balance of time consumption and quality, the average of five measurements is adapted for real remote sensing under the pulse laser conditions of wavelength 532 nm, pulse energy 650 mJ, repetition rate 10 Hz, pulse width 8 ns and linewidth 0.003 cm ⁻¹ (90 MHz). Measuring with the Brillouin linewidth has a better accuracy at a lower temperature (15 °C), based on the classical retrieval model we adopt. The experimental results show that the temperature error is 0.71 °C and 0.06 °C based on shift and linewidth respectively when the image SNR is at the range of 3.2 dB–3.9 dB.