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Rayleigh lidar equipped on airborne floating platforms has received increasing attention in recent years due to the demand for exploring the middle atmosphere. However, the inevitable attitude fluctuation of the platform affects the measurement accuracy of the photon profile, which greatly affects temperature retrieval. Here, an extensive theoretical analysis model of geometrical transformations between the actual altitude and detection distance under attitude fluctuations was constructed by taking pitch, roll, and observation angles into consideration. Based on this model and measured attitude angles, the influence of platform fluctuation on lidar measurement was analyzed by calculating the deviations between temperature retrieval results and the NRLMSISE-00 model at different observation angles, which demonstrated that the altitude displacement from the variation of pitch angle is a crucial factor in causing temperature retrieval error, especially at large observation angles. Then, an attitude compensation method was designed to eliminate the impact of fluctuations, incorporating the merits of good robustness. Under the observation angle of 45° and average pitch angle of around 4°, the maximum temperature deviation after attitude compensation was reduced from 21.29 K to 0.366 K, a reduction of around two orders of magnitude, indicating that the method can significantly improve the measurement accuracy of Rayleigh lidar.

Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. The fitness function F is formed by three objective functions: correlation coefficient R, regression stability coefficient S and mean fit deviation D. All three objective functions are obtained from the data itself and do not rely on prior information. The weights of the objective functions R, S and D are pre-trained by NRS, and IGWO is used to optimize the fitness function F. With ground-based aerosol lidar data, all-day signal gluing experiments are performed, where IGWO-RSD demonstrates obvious advantages in stability, accuracy and applicability in lidar signal processing compared with NRSWNSGA-II.

Measurement uncertainty is an extremely important parameter for characterizing the quality of measurement results. In order to measure the reliability of atmospheric temperature detection, the uncertainty needs to be evaluated. In this paper, based on the measurement models originating from the Chanin-Hauchecorne (CH) method, the atmospheric temperature uncertainty was evaluated using the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method (MCM) by considering the ancillary temperature uncertainty and the detection noise as the major uncertainty sources. For the first time, the GUM atmospheric temperature uncertainty framework was comprehensively and quantitatively validated by MCM following the instructions of JCGM 101: 2008 GUM Supplement 1. The results show that the GUM method is reliable when discarding the data in the range of 10–15 km below the reference altitude. Compared with MCM, the GUM method is recommended to evaluate the atmospheric temperature uncertainty of Rayleigh lidar detection in terms of operability, reliability, and calculation efficiency.

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively.

Under complex sea conditions, ship detection from remote sensing images is easily affected by sea clutter, thin clouds, and islands, resulting in unreliable detection results. In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network with a fully connected conditional random field. Based on the Resnet architecture, the remote sensing image is roughly segmented using a deep convolution neural network as the input. Using the Gaussian pairwise potential method and mean field approximation theorem, a conditional random field is established as the output of the recurrent neural network, thus achieving end-to-end connection. We compared the proposed method with other state-of-the-art methods on the dataset established by Google Earth and NWPU-RESISC45. Experiments show that the target detection accuracy of the proposed method and the ability of capturing fine details of images are improved. The mean intersection over union is 83.2% compared with other models, which indicates obvious advantages. The proposed method is fast enough to meet the needs for ship detection in remote sensing images.