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Amorphous indium-gallium-zinc-oxide (a-IGZO) thin film was deposited by radio frequency sputtering in an oxygen-free atmosphere. The composition of the a-IGZO thin film was adjusted to be In0.2Ga1.8ZnO4 so that the bandgap of the a-IGZO thin film was 4.5 eV for solar-blind ultraviolet photodetectors. Then, the bottom-gate IGZO phototransistor was fabricated with a high-k gate dielectric layer of hafnium oxide (HfO2) to enhance the gate controllability. The phototransistor showed excellent performance with an on/off ratio of $10^{{9}}$ and field-effect mobility of 5.4 cm $^{{2}}\cdot \text {V}^{-{1}}\cdot \text {s}^{-{1}}$ . Under the 255 nm illumination, the phototransistor presented a gate voltage-controlled ultraviolet response. With zero voltage gate bias, a responsivity of $3.2\times 10^{{5}}$ AW $^{-{1}}$ and a detectivity of $1.4\times 10^{{16}}$ Jones was obtained. As a phototransistor made from amorphous semiconductors, the response speed is very low. The rise time and decay time are estimated at about 12 s and over 1800 s, respectively. However, the response time was tremendously improved to be less than $25~\mu $ s by interleaving both positive and negative gate voltage pulses synchronized with the UV illumination. The synchronous gate voltage pulses can be easily realized in readout integrated circuits (ROICs). Therefore, a-IGZO thin-film phototransistor should be a good candidate for flexible solar-blind ultraviolet photodetector with excellent performance.

In recent years, enormous studies have been conducted to improve the land cover (LC) classification performance of multimodal remote sensing (RS) data, which outperforms single-modal-based methods by a large margin due to information diversity. To go a step further, we develop a two-branch patch-based convolutional neural network (CNN) with an encoder–decoder (ED) module to fuse multimodal RS data information. A knowledge distillation in model (DIM) module is proposed to guild per-modality encoder learning with the final fused information to enable multimodal data fusion more effectively. Moreover, utilizing multimodal information to guide single-modal learning still remains to be explored. To this end, a knowledge distillation cross-model (DCM) module is designed to improve single-modal LC classification with multimodal knowledge distillation, which bridges the gap between single-modal-based and multimodal-based methods. In particular, the multimodal-based method is taken as a teacher to transfer knowledge to single-modal-based methods. Extensive experiments are carried out on two multimodal RS datasets, including hyperspectral (HS) and light detection and ranging (LiDAR) data, i.e., the Houston2013 dataset, and HS and synthetic aperture radar (SAR) data, i.e., the Berlin dataset. The results demonstrate the effectiveness and superiority of the proposed multimodal fusion strategy in comparison with several state-of-the-art multimodal RS data classification methods. Also, the proposed DCM module improves the LC classification performance of single-modal methods by a large margin.

A spaceborne bistatic surveillance radar system, which may increase the target radar cross section through a large bistatic angle, can effectively improve the performance of small air moving target indication. However, due to the separation of the transmitter and the receiver, the clutter exhibits severe range dependence in both Doppler and spatial-angle domains. This may significantly decrease the number of independent identically distributed samples and, thus, cause the degradation of clutter suppression performance. In addition, when an air moving target flies above the sea, the broadened spectrum of sea clutter caused by the random intrinsic motion will further influence the clutter suppression performance. To deal with these problems, this article establishes a multichannel sea clutter model for a spaceborne bistatic surveillance radar system. The calculation of the positions of the iso-range contours is analyzed first. Then, the bistatic sea clutter model is established based on the directional wave spectrum, the monostatic–bistatic equivalence theorem, and the two-scale scattering model, where the Earth rotation and the range ambiguity of clutter are considered. Finally, the range dependence and the spectrum characteristics of bistatic sea clutter are discussed, and the clutter suppression performances under different azimuth bistatic angles, different elevation angles, and different sea states are analyzed. In addition, the influence of clutter range ambiguity is analyzed based on the simulations.