Advanced Information Mining from Ocean Remote Sensing Imagery with Deep Learning

In the past decades, the increasing ocean-research-oriented satellites, sensors, acquisition, and distribution channels have brought new tasks and challenges to mine information from such big data with complex and sparse information. The information mining requirements from big data and the advance in deep learning (DL) technology showed mutual promotive benefits in practical ocean information extraction and DL-based framework development. In 2020, scientists showed that most information retrievals from ocean remote sensing images could be accomplished using existing DL network frameworks, i.e., U-net for semantic segmentation and SSD (Single-Shot Multi-box Detection) for object detection [1]. The U-Net’s almost symmetric encoder-decoder structure and the skip connection between encoder-decoders have an excellent performance in retrieving fundamental semantic segmentation information in the ocean remote sensing imagery, such as coastal inundation area extractions [2]. SSD extracts feature maps of different data scales and takes a priori frames of different scales. Therefore, it has an excellent performance in detecting fundamental object detection problems in the ocean field, such as ship detection [3]. Although the off-the-shelf DL-based models are helpful, new developments in this field lead to a new era of DL-based technology for ocean remote sensing information mining. Specifically, two developments should be incorporated into the specific task-driven DL model: network architecture advance and domain-knowledge-based (expert knowledge) guidance in model parameter selection. Figure 1 upper panel shows the general framework used in [1] and the two newly added boxes that are the key elements we address in this paper.