This paper develops a two-dimensional array pyrometer, which can measure the true temperature field of the two-dimensional array. The pyrometer consists of an optical part, a circuit part and a software part. In the optical part, the radiation energy of the two-dimensional array target is obtained by scanning with the rotating mirror. Then, the radiation signal is converted and amplified by the circuit part. The software component realizes the functions of the pyrometer calibration, signal acquisition and data processing. The data processing adopts the secondary measurement method to calculate the true temperature and uses the multi-threaded method to improve the operational efficiency. Experiments show that the uncertainty of the two-dimensional pyrometer array can reach 1.43%. Compared with the single-threaded method, the true temperature operation time of the two-dimensional pyrometer array is improved by 77%, which verifies that the software operational efficiency is greatly improved.

Ship detection plays a pivotal role in numerous military and civil applications, yet detecting ships in complex maritime and aerial environments remains a challenging task. While several publicly available datasets for ship detection have been introduced by researchers, most of them do not adequately address the impacts of diverse and intricate environmental factors, which makes the trained algorithms difficult to apply for practical application scenes involving clouds, sea clutter, complex lighting, and facility interferences, limiting the effectiveness and robustness of the detection models. To advance the field of ship detection method research, we propose a high-quality dataset named Ship Collection in Complex Optical Scene (SCCOS), which is obtained from multiple platform sources including Google Earth, Microsoft map, Worldview-3, Pleiades, Orbview-3, Jilin-1 and Ikonos satellites. The dataset comprehensively considers complex scenes such as thin clouds, mist, thick clouds, light shadows, sea clutter, port facilities. Additionally, we conduct experiments on this dataset with eleven representative detection algorithms and establish a performance benchmark, which can provide the theoretical basis and practical reference for the design and optimization of subsequent ship detection models. The latest dataset is available at: https://github.com/JimmyRSlab/Dataset-and-Benchmark-for-Ship-Detection-in-Complex-Optical-Remote-Sensing-Image.

Based on the finite difference method and the artificial bee colony algorithm, the thermal conductivity in the two-dimensional unsteady-state heat transfer system is deduced. An improved artificial bee colony algorithm (IABCA), that artificial bee colony algorithm (ABCA) coupled with calculated deviation feedback, is proposed to overcome the shortcomings of insufficient local exploitation capacity and slow convergence rate in the late stage of the artificial bee colony algorithm (ABCA). For the forward problems, the finite difference method (FDM) is used to calculate the required temperature value of a discrete point; for the inverse problems, the IABCA is applied to minimize the objective function. In the inversion problem, the effects of colony size, number of measuring points, and the existence of measurement errors on the results are studied, and the inversion convergence rate of IABCA and ABCA is compared. The results demonstrate that the methods adopted in this paper had good effectiveness and accuracy even if colony sizes differ and measurement errors exist; and that IABCA has a more efficient convergence rate than ABCA.

Thin-film diffractive optical elements have considerable potential to be used in the field of high-resolution remote sensing imaging satellites because of advantages such as a large aperture, small volume, lightness, wide tolerance range of surface shape, and easy replication. However, there are problems associated with thin-film diffraction imaging, including space variation, serious blur, and low contrast, which result in insufficient imaging quality with regard to traditional optical system requirements. To address this, a local adaptive prior-based image restoration method is proposed for thin-film diffraction imaging systems. An entire degraded image was divided into several isohalo regions based on imaging characteristics. Then, the regularization constraints were adaptively selected and updated according to the local scene prior characteristics. Additionally, the system parameters in the corresponding field of view were used as input to restore each subregion. In particular, the diffraction efficiency was introduced into the model to remove the non-design level background radiation. The experimental results show that the proposed algorithm can effectively improve the image quality of a thin-film diffraction imaging system, including space variation correction, clarity enhancement, and background radiation suppression. Furthermore, a diffraction efficiency of less than 60% was found to significantly impact the final image products.