Oil spills are considered one of the major threats to the marine and coastal environment. Synthetic aperture radar (SAR) sensors are frequently employed for this purpose due to their ability to operate effectively under various weather and illumination conditions. SAR can clearly capture oil spills with distinctive radar backscatter intensity, resulting in dark regions in the images. This characteristic enables the monitoring and automatic detection of oil spills in SAR imagery. U-Net stands as one of the commonly employed semantic segmentation models, known for its ability to achieve superior segmentation performance even with limited training data. In this study, a modified lightweight U-Net model was introduced to enhance the performance of maritime multi-class segmentation in SAR images. First, a lightweight MobileNetv3 model served as the backbone for the U-Net encoder to perform feature extraction. Secondly, the convolutional block attention module (CBAM) was employed to enhance the network's capability in extracting multiscale features and to expedite the module calculation speed. The experimental results showed that the detection accuracy of the proposed method can achieve 77.07% of the mean Intersection-Over-Union (mIOU). Compared with the original U-Net model, the proposed architecture can improve the mIOU about 4.88%.

With the characteristics of high resolution, strong penetration ability, all-day observation and wide spatial coverage, Synthetic Aperture Radar (SAR) image has been widely used in shoreline detection. However, the shoreline detection will be affected by the shadows of geometric distortion caused by the side-looking of SAR especially in areas with large terrain fluctuations, such as the eastern coast of Taiwan. Therefore, this study proposed an efficient shoreline detection method using dual-polarization Sentinel-1 SAR and digital elevation model (DEM) data based on deep learning methods. In this research, two common self-built datasets were introduced, which covered all coastal areas of the study area in Taiwan island. The datasets included a total of 4,029 and 3,522 images, respectively. One contains VH polarization images and the other consists of a three-layer stack with VH, VV polarization images and DEM data. The training images of the dataset were labeled by manual inspection and morphological processing. In this study, the shoreline detection was based on the semantic segmentation U-Net model with batch normalization (BN) module. The segmentation results of the U-Net model were then processed by morphological postprocessing and edge detection for shoreline detection. Experimental results show that the combination of dual-polarization SAR and DEM data significantly improves shoreline detection results compared to those using Sentinel-1 VH imagery.

This study proposed a paddy rice mapping based on hand-crafted features combined with deep learning methods. In this research, the rice growth-related features were extracted from time-series SAR data provided by C-band Sentinel-1A images. Four rice features were first extracted from the rice growth curve, including Average Normalized Backscatter (ANB), Backscatter Variation Rate (BVR), Time Interval (TI) and Backscatter Difference (BD). Then, the deep learning U-Net model with four combined rice features was used to obtain the mapping distribution of paddy rice-fields. In the study, the experimental areas were composed of two important rice growth counties in central Taiwan, including Yunlin and Changhua counties. The experimental results showed that the detection accuracy of the proposed method for rice and non-rice can achieve 92.3% and 98.2%, respectively. These results demonstrate the great potential of SAR data in mapping paddy fields using the U-Net model with proposed feature inputs.

Taiwan's agriculture is an important national economic industry. Ensuring food security and stabilizing the food supply are the government's primary goals. The Agriculture and Food Agency (AFA) of the Executive Yuan's Council of Agriculture has conducted agricultural and food surveys to address those issues. Synthetic aperture radar (SAR) images will not be affected by climatic factors, which makes them more suitable for the forecast of rice production. This research uses the spatial-temporal neural network convolutional long short-term memory network (ConvLSTM) to identify rice fields from SAR images. The results show that ConvLSTM can greatly reduce the proportion of model false positives to 51.16%, produced higher average precision of 95.70%, and F1-score of 0.9648. The ConvLSTM neural network has produced good results for rice field identification compared with state-of-the-art neural networks.

Ship detection is one of the most important researches in the field of navigation safety and marine environment monitoring. Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities. However, the resolution of SAR images is limited, and it cannot effectively detect densely distributed and small ships, especially near harbors. In order to effectively manage ships during the day and night, this research uses visible and infrared images for ship detection. In this study, an improved architecture based on You only look once version 3 (Yolov3) is proposed for ship detection. Yolov3 provided multi-scale feature extraction to enhance the recognition of small targets. In addition, the study also considered the influence of Yolov3 parameters on ship detection, including the input image size, the number of filters in convolution layers and the detection scales. The experiment is based on a data set containing six types of ships, and a total of 5, 513 visible and infrared images from the harbors in northern Taiwan. The experimental results show that when the model parameters are selected as: 352x352 input size, the scale of large target deleted and the convolution filters reduced by 30%, Yolov3 has better ship detection performance and computational efficiency. Compared with the original Yolov3 with 87.9% mean average precision (mAP) and 87.0 billion floating point operations per second (BFLOPs), the proposed architecture can achieve 89.1 % mAP and 24.3 BFLOPs.

Rice is the most important food source for many countries, which especially for Asia, includes Taiwan. Monitoring rice field distribution can effectively manage food security. This study proposed a feature-based decision approach to detect the mapping of rice cultivation using the time-series Synthetic Aperture Radar (SAR) data provided by Sentinel-1A. Instead of using the maximum and minimum backscatter of SAR data, as most studies in the literature, this study established a rice growth model based on complete time series data in the rice growth period. From the developed model, a feature related to rice growth time, that is, the time interval (TI) between vegetative growth and maturity stages was introduced. The proposed feature was first compared with the feature of backscatter difference (BD) between the maximum and minimum value of SAR data. The experimental results show that the proposed feature can achieve better rice detection accuracy. Then, a decision method based on the combination of TI and BD features was proposed for rice planting mapping. In the study, Yunlin and Changhua counties in central Taiwan were used as experimental areas. The experimental results show that the proposed method can achieve more than 90% overall accuracy in rice detection for VH polarization. Furthermore, comparing with the traditional method that uses growth height feature, BD, the proposed method can improve the overall accuracy of rice detection about 5%.