This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.

Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network’s competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.

As the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the processing efficiency and detection accuracy at the same time, the study presented an improved deep-learning network based on You only look once version 3 (Yolov3) for all-day ship detection with visible and infrared images. Yolov3 network can simultaneously improve the recognition ability of large and small objects through multiscale feature-extraction architecture. Considering reducing computational time and network complexity with relatively competitive detection accuracy, the study modified the architecture of Yolov3 by choosing an appropriate input image size, fewer convolution filters, and detection scales. In addition, the reduced Yolov3 was further modified with the spatial pyramid pooling (SPP) module to improve the network performance in feature extraction. Therefore, the proposed modified network can achieve the purpose of multi-scale, multi-type, and multi-resolution ship detection. In the study, a common self-built data set was introduced, aiming to conduct all-day and real-time ship detection. The data set included a total of 5557 infrared and visible light images from six common ship types in northern Taiwan ports. The experimental results on the data set showed that the proposed modified network architecture achieved acceptable performance in ship detection, with the mean average precision (mAP) of 93.2%, processing 104 frames per second (FPS), and 29.2 billion floating point operations (BFLOPs). Compared with the original Yolov3, the proposed method can increase mAP and FPS by about 5.8% and 8%, respectively, while reducing BFLOPs by about 47.5%. Furthermore, the computational efficiency and detection performance of the proposed approach have been verified in the comparative experiments with some existing convolutional neural networks (CNNs). In conclusion, the proposed method can achieve high detection accuracy with lower computational costs compared to other networks.

Climate change and global warming lead to changes in the sea level and shoreline, which pose a huge threat to island regions. Therefore, it is important to effectively detect the shoreline changes. Taiwan is a typical island, located at the junction of the East China Sea and the South China Sea in the Pacific Northwest, and is deeply affected by shoreline changes. In this research, Taiwan was selected as the study area. In this research, an efficient shoreline detection method was proposed based on the semantic segmentation U-Net model using the Sentinel-1 synthetic aperture radar (SAR) data of Taiwan island. In addition, the batch normalization (BN) module was added to the convolution layers in the U-Net architecture to further improve the generalization ability of U-Net and accelerate the training process. A self-built shoreline dataset was introduced to train the U-Net model and test its detection efficiency. The dataset consists of a total of 4029 SAR images covering all coastal areas of Taiwan. The training samples of the dataset were annotated by morphological processing and manual inspection. The segmentation results of U-Net were then processed by edge detection and morphological postprocessing to extract the shoreline. The experimental results showed that the proposed method could achieve a satisfactory detection performance compared with the related methods using the data provided by the Ministry of the Interior of Taiwan from 2016 to 2019 for different coastal landforms in Taiwan. Within a 5-pixel difference between the detected shoreline and the ground truth data, the F1-Meaure of the proposed method exceeded 80%. In addition, the potential of this method in shoreline change detection was validated with a sandbar located on the southwestern coast of Taiwan. Finally, the entire shoreline of Taiwan has been described by the proposed approach and the detected shoreline length was close to the actual length.

This study improved the shoreline detection performance based on the U-Net model by combining Sentinel-1 synthetic aperture radar (SAR) and digital elevation model (DEM) data. The U-Net network was first modified to enhance feature extraction by using the MobileNetV3 backbone architecture and convolutional block attention module. To alleviate the performance degradation of shoreline detection caused by radar shadow, especially in coastal areas with large terrain undulations, SAR and DEM data were combined as input to U-Net. Furthermore, this study evaluated the shoreline detection performance using the statistical analysis based on the proposed probabilistic model of distance difference between the detected shoreline and reference data which was provided by Construction and Planning Agency Ministry of the Interior, Taiwan government. The experiment was conducted based on two self-built datasets, one containing 4061 SAR images and the other containing 3822 SAR images and corresponding DEM data, both collected in the coastal areas of Taiwan from 2016 to 2019. The experimental results showed that compared with the U-Net network using SAR data, the modified U-Net has achieved superior performance in shoreline detection for various coastal landforms. Moreover, the addition of DEM data reduced the influence of radar shadow, making shoreline detection results more consistent with reference data. Finally, the generalization ability of the modified U-Net in shoreline detection was also verified by testing images from regions not included in the built dataset.

This study proposed an improved full-scale aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas using synthetic aperture radar (SAR) images. The convolutional block attention module (CBAM) in the FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model’s overconfidence in majority class samples while improving the model’s generalization ability. The detection performance of the improved FA-MobileUNet model reached an mIoU (mean intersection over union) of 84.55%, which was 17.15% higher than that of the original U-Net model. The effectiveness of the proposed model was then verified using the oil pollution incidents that significantly impacted Taiwan’s marine environment. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.