Transformer models have great potential in the field of remote sensing super-resolution (SR) due to their excellent self-attention mechanisms. However, transformer models are prone to overfitting because of their large number of parameters, especially with the typically small remote sensing datasets. Additionally, the reliance of transformer-based SR models on convolution-based upsampling often leads to mismatched semantic information. To tackle these challenges, we propose an efficient super-resolution hybrid network (EHNet) based on the encoder composed of our designed lightweight convolution module and the decoder composed of an improved swin transformer. The encoder, featuring our novel Lightweight Feature Extraction Block (LFEB), employs a more efficient convolution method than depthwise separable convolution based on depthwise convolution. Our LFEB also integrates a Cross Stage Partial structure for enhanced feature extraction. In terms of the decoder, based on the swin transformer, we innovatively propose a sequence-based upsample block (SUB) for the first time, which directly uses the sequence of tokens in the transformer to focus on semantic information through the MLP layer, which enhances the feature expression ability of the model and improves the reconstruction accuracy. Experiments show that EHNet’s PSNR on UCMerced and AID datasets obtains a SOTA performance of 28.02 and 29.44, respectively, and is also visually better than other existing methods. Its 2.64 M parameters effectively balance model efficiency and computational demands.

The identification of hazardous materials is a key measure in the prevention and control of fire and explosion disasters. Conventional techniques used to identify hazardous materials include contact detection and post-sampling laboratory testing, which cannot meet the needs of extreme environments, where personnel and equipment are not accessible for on-site detection. To address this problem, this paper proposes a method for the classification and identification of hazardous materials based on convolutional neural networks, which can achieve non-contact remote detection of hazardous materials. Firstly, a dataset containing 1800 hyperspectral images of hazardous materials, which can be used for deep learning, is constructed based on the hazardous materials hyperspectral data cube. Secondly, based on this, an improved ResNet50-based classification method for hazardous materials is proposed, which innovatively utilizes a classification network based on offset sampling convolution and split context-gated convolution. The results show that the method can achieve 93.9% classification accuracy for hazardous materials, which is 1% better than the classification accuracy of the original ResNet50 network. The network also has high performance under small data volume conditions, effectively solving the problem of low classification accuracy due to small data volume and blurred image data features of labelled hazardous material images. In addition, it was found that offset sampling convolution and split context-gated convolution showed synergistic effects in improving the performance of the network.

We propose a new spectral super-resolution technique combined with a Fabry–Perot interferometer (FPI) and an interferometric hyperspectral imager. To overcome the limitation of the maximal optical path difference (OPD) on the spectral resolution, the object spectrum is periodically modulated based on the FPI, and an optical Fourier transform of the modulated spectrum information is performed using a double-beam interferometer to obtain an interferogram. Drawing on the concept of nonlinear structured light microscopy, the displacement of the high-frequency interference information in the interferogram after adding the FPI is analyzed to restore the high-frequency interference information and improve the spectral resolution. The optical system has a compact structure with little impact on complexity, spectral range, or luminous flux. Our simulation results show that this method can realize multicomponent joint-interference imaging to obtain spectral super-resolution information. The effects of the FPI’s reflectance and interval are analyzed, and the reflectance needs to be within 20~80% and the interval must be as close as possible to the maximum optical range of the interferometer. Compared with previous, related innovations, this innovation has the advantages of higher system stability, higher data utilization, and better suitability for interferometric imaging spectrometers.

High-precision detection of rice targets in precision agriculture is crucial for yield assessment and field management. However, existing models still face challenges, such as high rates of missed detections and insufficient localization accuracy, particularly when dealing with small targets and dynamic changes in scale and morphology. This paper proposes an accurate rice detection model for UAV images based on Adaptive Aware Dynamic Convolution, named Adaptive Dynamic Convolution YOLO (ADC-YOLO), and designs the Adaptive Aware Dynamic Convolution Block (ADCB). The ADCB employs a “Morphological Parameterization Subnetwork” to learn pixel-specific kernel shapes and a “Spatial Modulation Subnetwork” to precisely adjust sampling offsets and weights—realizing for the first time the adaptive dynamic evolution of convolution kernel morphology with variations in rice scale. Furthermore, ADCB is embedded into the interaction nodes of the YOLO backbone and neck; combined with depthwise separable convolution in the neck, it synergistically enhances multi-scale feature extraction from rice images. Experiments on public datasets show that ADC-YOLO comprehensively outperforms state-of-the-art algorithms in terms of AP50 and AP75 metrics and maintains stable high performance in scenarios such as small targets at the seedling stage and leaf overlap. This work provides robust technical support for intelligent rice field monitoring and advances the practical application of computer vision in precision agriculture.

The identification of hazardous chemicals based on hyperspectral imaging is an important emergent means for the prevention of explosion accidents and the early warning of secondary hazards. In this study, we used a combination of spectral curve matching based on full-waveform characteristics and spectral matching based on spectral characteristics to identify the hazardous chemicals, and proposed a method to quantitatively characterize the matching degree of the spectral curves of hazardous chemicals. The results showed that the four hazardous chemicals, sulfur, red phosphorus, potassium permanganate, and corn starch had bright colors, distinct spectral curve characteristics, and obvious changes in reflectivity, which were easy to identify. Moreover, the matching degree of their spectral curves was positively correlated with their reflectivity. However, the spectral characteristics of carbon powder, strontium nitrate, wheat starch, and magnesium–aluminum alloy powder were not obvious, with no obvious characteristic peaks or trends of change in reflectivity. Except for the reflectivity and the matching degree of the carbon powder being maintained at a low level, the reflectivity of the remaining three samples was relatively close, so that it was difficult to identify with the spectral curves alone, and color information should be considered for further identification.

High-precision three-dimensional (3D) imaging is essential for accurately perceiving environments, providing critical depth and spatial awareness. Among various approaches, solid-state LiDAR systems have garnered significant attention. However, depth precision, detection range and pixel scalability remain key challenges for their widespread adoption. Here, we report a large-array coherent flash 3D imaging system that achieves a sub-millimeter range precision through stepped-frequency modulation and coherent detection with CCD sensors. A coherent image sensor is developed, and a prototype system is demonstrated, providing 3D imaging with a depth precision as high as 0.47 mm over a range of 30.50 m at an optical power of 15.86 mW and a maximum frame rate of 10 Hz. Our system features high range precision, exceptional sensitivity across long distances, and robust pixel scalability by directly leveraging well-established CCD sensors. This advancement introduces a scalable approach to long-range high-precision 3D imaging, with substantial implications for deformation monitoring, virtual reality, and cultural heritage preservation.