Deep learning (DL) has shown great capability in remote sensing and automatic target recognition (ATR). However, huge computational costs and power consumption are challenging the development of current DL methods. Optical neural networks have recently been proposed to provide a new mode to replace artificial neural networks. Here, we develop a residual optronic convolutional neural network (res-OPCNN) for synthetic aperture radar (SAR) recognition tasks. We implement almost all computational operations in optics and significantly decrease the network computational costs. Compared with digital DL methods, res-OPCNN offers ultra-fast speed, low computation complexity, and low power consumption. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the lightweight nature of the optronic method and its feasibility for SAR target recognition.

Optically pumped magnetometers (OPM) are core quantum payloads for geomagnetic remote sensing. Among them, the spin-exchange relaxation-free (SERF) OPM with aT-level ultimate sensitivity stands as mainstream. While enlarging the alkali-metal vapor cell of the SERF OPM enhances sensitivity, it triggers complex atomic spin relaxation, notably intensified magnetic field gradient relaxation. To address the dilemma of atomic spin relaxation regulation and the engineering requirements of ultra-high-sensitivity SERF magnetometers, this paper constructs an analytical model of the total relaxation rate that comprehensively considers wall-collision relaxation, spin-destruction collision relaxation, and longitudinal/transverse magnetic field gradient relaxation, etc. The analytical relationship between buffer gas pressure and total relaxation rate for a commonly used spherical vapor cell is derived, revealing the intrinsic correlation among cell size, atomic spin relaxation, and optimal pressure. Based on the theoretical model, the filling parameters of the vapor cell are optimized, and experimental measurements are carried out. The theoretical relaxation results are highly consistent with the experimental ones, realizing the precise optimization of buffer gas pressure. The optimization method proposed in this paper provides a theoretical basis and parameter guidance for the engineering preparation of alkali-metal vapor cells for high-sensitivity SERF magnetometers in remote sensing applications.