The change detection of urban buildings is currently a hotspot in the research area of remote sensing, which plays a vital role in urban planning, disaster assessments and surface dynamic monitoring. SAR images have unique characteristics compared with traditional optical images, mainly including abundant image information and large data volume. However, the majority of currently used SAR images for the detection of changes in buildings have the problems of missing the detection of small buildings and poor edge segmentation. Therefore, this paper proposes a new approach based on deep learning for changing building detection, which we called CD-TransUNet. It should be noted that CD-TransUNet is an end-to-end encoding–decoding hybrid Transformer model that combines the UNet and Transformer. Additionally, to enhance the precision of feature extraction and to reduce the computational complexity, the CD-TransUNet integrates coordinate attention (CA), atrous spatial pyramid pooling (ASPP) and depthwise separable convolution (DSC). In addition, by sending the differential images to the input layer, the CD-TransUNet can focus more on building changes over a large scale while ignoring the changes in other land types. At last, we verify the effectiveness of the proposed method using a pair of ALOS-2(L-band) acquisitions, and the comparative experimental results obtained from other baseline models show that the precision of the CD-TransUNet is much higher and the Kappa value can reach 0.795. Furthermore, the low missed alarms and the accurate building edge reflect that the proposed method is more appropriate for building changing detection tasks.

Lodging, a commonly occurring rice crop disaster, seriously reduces rice quality and production. Monitoring rice lodging after a typhoon event is essential for evaluating yield loss and formulating suitable remedial policies. The availability of Sentinel-1 and Sentinel-2 open-access remote sensing data provides large-scale information with a short revisit time to be freely accessed. Data from these sources have been previously shown to identify lodged crops. In this study, therefore, Sentinel-1 and Sentinel-2 data after a typhoon event were combined to enable monitoring of lodging rice to be quickly undertaken. In this context, the sensitivity of synthetic aperture radar (SAR) features (SF) and spectral indices (SI) extracted from Sentinel-1 and Sentinel-2 to lodged rice were analyzed, and a model was constructed for selecting optimal sensitive parameters for lodging rice (OSPL). OSPL has high sensitivity to lodged rice and strong ability to distinguish lodged rice from healthy rice. After screening, Band 11 (SWIR-1) and Band 12 (SWIR-2) were identified as optimal spectral indices (OSI), and VV, VV + VH and Shannon Entropy were optimal SAR features (OSF). Three classification results of lodging rice were acquired using the Random Forest classification (RFC) method based on OSI, OSF and integrated OSI–OSF stack images, respectively. Results indicate that an overall level of accuracy of 91.29% was achieved with the combination of SAR and optical optimal parameters. The result was 2.91% and 6.05% better than solely using optical or SAR processes, respectively.

Unlike optical satellites, synthetic aperture radar (SAR) satellites can operate all day and in all weather conditions, so they have a broad range of applications in the field of ocean monitoring. The ship targets’ contour information from SAR images is often unclear, and the background is complicated due to the influence of sea clutter and proximity to land, leading to the accuracy problem of ship monitoring. Compared with traditional methods, deep learning has powerful data processing ability and feature extraction ability, but its complex model and calculations lead to a certain degree of difficulty. To solve this problem, we propose a lightweight YOLOV5-MNE, which significantly improves the training speed and reduces the running memory and number of model parameters and maintains a certain accuracy on a lager dataset. By redesigning the MNEBlock module and using CBR standard convolution to reduce computation, we integrated the CA (coordinate attention) mechanism to ensure better detection performance. We achieved 94.7% precision, a 2.2 M model size, and a 0.91 M parameter quantity on the SSDD dataset.

To verify the performance of the high-resolution fully polarimetric synthetic aperture radar (SAR) sensor carried by the Xinzhou 60 remote-sensing aircraft, we used corner reflectors to calibrate the acquired data. The target mechanism in high-resolution SAR images is more complex than it is in low-resolution SAR images, the impact of the point target pointing error on the calibration results is more obvious, and the target echo signal of high-resolution images is more easily affected by speckle noise; thus, more accurate extraction of the point target position and the response energy is required. To solve this problem, this paper introduces image context information and proposes a method to precisely determine the integration region of the corner reflector using sliding windows based on the integral method. The validation indicates that the fully polarimetric SAR sensor on the Xinzhou 60 remote-sensing aircraft can accurately reflect the radiometric characteristics of the ground features and that the integral method can obtain more stable results than the peak method. The sliding window allows the position of the point target to be determined more accurately, and the response energy extracted from the image via the integral method is closer to the theoretical value, which means that the high-resolution SAR system can achieve a higher radiometric calibration accuracy. Additionally, cross-validation reveals that the airborne SAR images have similar quality levels to Sentinel-1A and Gaofen-3 images.

Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in‐depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)‐assisted object detection model for cell phenotype screening and a cross‐interface contact method for single‐cell exporting, we developed an automatic and index‐based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, “One‐Cell‐One‐Tube” manner. The target cell is automatically identified based on an AI‐assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross‐interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single‐cell culture or sequencing. The efficiency of the system for single‐cell printing is >93%. The throughput of the system for single‐cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI‐assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single‐cell proliferation assays. The automation, index maintenance, and vitality preservation of EasySort AUTO suggest its excellent application potential for single‐cell sorting.

C-SAR/01, the successor of China’s Gaofen-3 Satellite, which launched on 23 November 2021, is the first synthetic aperture radar (SAR) satellite to be launched in China’s civil space infrastructure plan and has served as an invaluable data resource. Radiometric calibration and validation are prerequisites for the quantitative application of SAR data. In this study, the radiometric calibration experiments of C-SAR/01 data of the ultra-fine strip (UFS) and fine strip I (FSI) modes were conducted applying flexible automatic triangular trihedral corner reflectors deployed in Xilinhot SAR satellite calibration and validation site. Accordingly, the image quality and radiometric calibration accuracy were evaluated. The results show that the spatial resolution, peak sidelobe ratio, and integrated sidelobe ratio of UFS and FSI mode data of C-SAR/01 are better than those of the design indexes, and the calibration results from the integral method are more stable than those from the peak method. Furthermore, the standard deviation of the calibration constant for UFS mode data is 0.234 dB, with the relative and absolute calibration accuracies obtained as 0.233 and 0.532 dB, respectively, whereas the standard deviation calibration constant for FSI mode data is 0.198 dB, with its relative and absolute calibration accuracies evaluated as 0.199 and 0.333 dB, respectively.