Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.

The large number of bands, the huge amount of information and the high band correlation in hyperspectral images bring great difficulties to the band selection of hyperspectral images. In the basic Particle Swarm Optimization (PSO), the learning factor and inertia factor are fixed, which limits the exploratory ability of the algorithm and cannot balance the global and local relations well. Therefore, a new algorithm-Improved Quantum Evolutionary Particle Swarm Optimization (IQEPSO) is proposed that the learning factor and inertia factor could be changed with the number of iterations. At the same time, mutation probability could be changed with the number of invariable fitness values, in order to enable particles to jump out of the local optimum. The proposed algorithm is applied to band selection of hyperspectral images. Experiments show that the proposed algorithm can improve the classification accuracy of ground objects. The disadvantage of falling into local optimum is overcome, the convergence speed is accelerated, and better classification accuracy is obtained.

Synthetic Aperture Radar is an active remote sensing system that possesses characteristics such as all-weather capability and strong penetrability. In ship detection, SAR is widely used in military and civilian applications due to its excellent properties. However, in practical applications, SAR often faces challenges in achieving ideal imaging results due to factors such as height and sea conditions. Although deep learning-based object detection methods have achieved promising results in SAR ship detection, the small size of ship targets and complex sea clutter make it difficult to further improve the precision and recall rates of such algorithms. To address the challenge of balancing global and local features using convolution-based algorithms, this paper proposes a detection algorithm that integrates convolution and transformers. Firstly, an improved transformer module is designed, which fuses the output feature maps of the traditional convolution module and the improved transformer module through a parallel structure. Then, a novel backbone structure is developed by stacking the aforementioned parallel structures, enabling the integration of global and local features as well as the detection of multi-scale features. Experimental results demonstrate that compared to other models, the proposed method achieves more effective SAR detection in SAR images.

In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convolution algorithm are combined to enrich the details; then, a regularization function with two adaptive parameters and two regularization terms is proposed to enhance the edge. MPSR firstly enhances the multi-scale detail of low-resolution images; then, regular processing and feature extraction are carried out; finally, sub-pixel convolution is used to fuse the extracted features to generate high-resolution images. The experimental results show that the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results.

Based on spectrum analysis technology, a fast demodulation method is proposed to sense the acoustic and temperature with EFPI-FBG hybrid optical microphone accurately. Separating the EFPI and FBG spectrum from the obtained EFPI-FBG superimposed spectrum, frequency estimation based on total phase method and peak-searching based on quadratic polynomial fitting method were adopted to calculate the F-P cavity’s length and the central wavelength of the FBG. The detailed simulation showed that the proposed method has the capabilities to detect the change of the F-P cavity’s length and the drift of the FBG’s central wavelength with a high speed and accuracy. By applying the proposed method to a homemade diaphragm-based EFPI-FBG hybrid optical microphone, the acoustic and temperature could be recovered successfully. The acoustic sensing error caused by the temperature could be reduced from 75% to 10%.

In order to realize the detection and recognition of drones in environments characterized by severe electromagnetic interference, a novel approach combined with the fiber-optic external Fabry-Perot interferometric (EFPI) acoustic sensor and deep learning is proposed in this paper. A fiber-optic EFPI acoustic sensor based on graphene diaphragm is designed and produced. Its phase sensitivity is -94.15 dB re 1 rad/μPa@7kHz, which ensures high-sensitivity detection and recognition of drones. The fast white light interference demodulation algorithm is used to demodulate the acoustic signals of different drones from the spectra. Then, the mel-scale frequency cepstral coefficients (MFCC) of acoustic signals are extracted as inputs for a network model combining convolutional neural network and long short-term memory (CNN-LSTM) for detecting and recognizing drones. The experimental results demonstrate that the accuracy of the method for detecting drones can reach 99.50%, and the accuracy for recognizing drones can reach as high as 97.52%.