In this article, we present two fast and interpretable decomposition methods for 2D homography, which are named Similarity-Kernel-Similarity (SKS) and Affine-Core-Affine (ACA) transformations respectively. Under the minimal 4-point configuration, two similarity transformations in SKS are computed by two anchor points on source and target planes, respectively. Then, the other two point correspondences can be exploited to compute the middle kernel transformation with only four parameters. Furthermore, ACA uses three anchor points to compute the source and the target affine transformations, followed by computation of the middle core transformation utilizing the other one point correspondence. ACA can compute a homography up to a scale with only 85 floating-point operations (FLOPs), without even any division operations. Therefore, as a plug-in module, ACA facilitates various traditional feature-based Random Sample Consensus (RANSAC) pipelines, as well as deep homography pipelines estimating 4-point offsets. In addition to the advantages of geometric parameterization and computational efficiency, SKS and ACA can express each element of homography by a polynomial of input coordinates (7th degree to 9th degree), extend the existing essential Similarity-Affine-Projective (SAP) decomposition and calculate 2D affine transformations in a unified way.

Considering the extremely low signal-to-noise ratio (SNR) environment in future satellite internet of Things (IoT) transmission in which the traditional Hamming coded LoRa (Long Range) system in the ground IoT communication is of difficulty to guarantee high reliability, optimal coding scheme selection for the LoRa-based satellite IoT is studied for the first time. In specific, the corresponding system model is described in detail, followed by derivations of two noncoherent detection methods of coded LoRa signals. Subsequently, the channel capacity of the coded LoRa system is provided and used to develop the corresponding relationship between the signal-to-noise ratio (SNR) and the code rate. Based on the obtained relationship and the Monte-Carlo method, it is found that the coded LoRa system with different spreading factors (SFs) can achieve the lowest SNR when the code rate is close to 1/2. Finally, the performance of several coded LoRa systems (including Hamming code, BCH code, RS code, Turbo code, LDPC code and LDGM code with code rates of about 1/2) is compared by means of the Monte-Carlo method. Simulation results show that Turbo or LDPC coded LoRa systems exhibit more than 4.5 dB performance advantages over the Hamming coded LoRa system.

In recent years, multiple synthetic aperture radar (SAR) satellites, such as Radarsat-2 and Gaofen-3 (GF-3) spaceborne SAR systems have adopted multichannel high-resolution and wide-swath (MC-HRWS) imaging technology, which can overcome the resolution and mapping width limitation of single-channel SAR. However, considering the influence of Doppler velocity of moving targets and signal energy outside the processed Doppler bandwidth (PDB) on azimuth signal reconstruction processing, Doppler distortion of moving targets and ambiguities energy will occur in the final single-look complex (SLC) data. These issues will result in significant Doppler centroid error and high azimuth ambiguity-to-signal ratio (AASR), severely restricting the ability to obtain accurate ocean surface Doppler velocities. Therefore, an accurate ocean Doppler velocity estimation method for the MC-HRWS SAR system was proposed based on a reconstruction algorithm in the imaging process and the azimuth ambiguity signal model. According to the Bayesian principle, the problem of estimating Doppler velocity on the ocean surface has been transformed into an optimization problem for maximizing the Doppler parameters of posterior probability. Simulation comparative experiments revealed that our method outperformed the conventional methods in the cases of low-uniform sampling, particularly in the region with high AASR. In addition, the Doppler velocity estimation results of the GF-3 ultrafine strip-map (UFS) mode also indicate that the proposed method has a good suppression effect on the Doppler velocity estimation error and AASR for the MC-HRWS SAR system.

The accurate interpretation of ocean waves in synthetic aperture radar (SAR) images is challenging because the scattering of moving waves causes various complex mechanisms. Moreover, SAR imaging is based on a unique nonlinear mapping relationship. This study proposed a full-link simulator to simulate the main parts of ocean surface backscattering, ocean surface motion, platform motion, echo generation and acquisition, SAR imaging, etc. The proposed simulator demonstrated the dynamic characteristics of the ocean surface by simulating the movement and decoherence effect of scattering cells within each radar pulse. Subsequently, a method for retrieving the wave direction spectrum from the SAR image spectrum based on the simulator was developed. The simulator, which can accurately reflect the modulation of backscattering, velocity bunching effect, and decoherence effect, was used as the forward transformation from the ocean wave spectrum to SAR image spectrum. In addition, the Levenberg-Marquardt iterative method was applied to determine the optimal solution through adjustment of several key parameters of the wave spectrum and ocean surface coherence time. Further, the Sentinel-1 SAR data were used to verify the accuracy of the full-link simulator and the effectiveness of the retrieval scheme. The results showed the following. 1) The SAR image with the simulation of both the moving scene and decoherence effect was highly consistent with the SAR image observed by Sentinel-1. 2) The retrieval scheme exhibited strong correction ability, thereby effectively improving the consistency between the retrieved and observed SAR spectra. In addition, the wave parameters (significant wave height, mean wave period, and dominant wave propagation direction) calculated considering the retrieved wave spectrum were consistent with the buoy measurements.

Tire pattern classification is an important means to provide clues for traffic accident processing. With the rapid increase in the number of vehicles, it is in urgent need to develop efficient and automatic tire pattern image classification and recognition system. In this paper, we design a novel lightweight network model for tire pattern image classification dataset. The proposed method consists of two parts. On the one hand, the powerful ConvNeXt is used as the main baseline. On the other hand, the attention mechanism is introduced without sacrificing FLOPs, which improves the classification accuracy. Experimental results on the Tire pattern dataset demonstrate that, the proposed model performed satisfactorily when tested against low quality tire patterns taken from actual scenarios. In addition, experimental analysis of ablation demonstrates the good robustness of the proposed method.