the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Jul 2024
SAR Image Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena
Abstract. The ocean surface exhibits a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is crucial for understanding oceanic dynamics and ocean-atmosphere interactions. In this study, we select 2,383 Sentinel-1 WV mode images and 2,628 IW mode sub-images to construct a semantic segmentation dataset that includes 12 typical oceanic and atmospheric phenomena. Each phenomenon is represented by approximately 400 sub-images, resulting in a total of 5,011 images. The images in this dataset have a resolution of 100 meters and dimensions of 256 × 256 pixels. We propose a modified Segformer model to segment semantically these multiple categories of oceanic and atmospheric phenomena. Experimental results show that the modified Segformer model achieves an average Dice coefficient of 80.98 %, an average IoU of 70.32 %, and an overall accuracy of 87.13 %, demonstrating robust segmentation performance of typical oceanic and atmospheric phenomena in SAR images.
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RC1: 'Comment on essd-2024-222', Anonymous Referee #1, 07 Aug 2024
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I appreciate the effort and work put into this manuscript, which focuses on constructing a dataset of SAR images annotated for 12 types of oceanic and atmospheric phenomena and developing a deep learning model to segment these phenomena. The paper addresses a significant topic and provides valuable contributions. However, several areas need to be addressed to improve the overall quality and clarity of the manuscript.
1. Dataset
1) Ground Truth Determination: The criteria for determining the boundaries of each phenomenon are not clearly defined. For example, the internal wave is identified by its wave crest lines, while the pure ocean wave includes both wave crest lines and surrounding seawater. The boundary size for eddies is not clearly defined, and typically, eddies detected by SAR are accompanied by biological slicks, which are not considered in the dataset. The sea ice regions in the images seem to include ice leads, yet the entire area is labeled as sea ice. Additionally, the separation between low wind speed areas and biological slicks or oil spills is not clearly explained. A more rigorous and transparent method for defining these boundaries is needed.
2) Sample Diversity: Compared to the Sentinel-1 SAR dataset by Wang et al. (2019), this manuscript adds internal waves and eddies, but uses the IW mode data, which primarily captures nearshore areas. Internal waves and eddies, particularly eddies, typically occur offshore. Using IW mode data limits the representation of these phenomena. Additionally, the manuscript mentions using 484 IW images to select samples of internal waves and eddies, but it is unclear where these images are located, how representative they are, and the criteria for their selection.
3) Geographical Information: Oceanic and atmospheric phenomena vary significantly with the scale and the region of occurrence. The TenGeo-SAR dataset (Wang et al., 2019), on which this study builds, does not provide geographical information, making it difficult for readers or users to assess the representativeness of the images. Except for the final rainfall image, the manuscript does not provide geographical coordinates for all the SAR images, hindering reproducibility and assessment of the data’s representativeness.
2. Deep Learning Model
1) Metric Calculation: Many of the selected phenomena, such as fronts, internal waves, and icebergs, are significantly smaller in pixel count compared to the background (seawater). The manuscript does not exclude the background when calculating metrics, leading to potentially inflated performance scores. A model that outputs only seawater would achieve high scores under these conditions. A more accurate evaluation would exclude the background from metric calculations.
2) Segmentation Accuracy: The manuscript includes full SAR images for testing the model, which is commendable. However, in the case of internal wave extraction (Figures 12 and 13), several rain cells are visible but not identified by the model. The manuscript should include a comparison with ground truth and corresponding metrics for all phenomena. Additionally, the rationale for selecting only internal waves and rain cells for demonstration should be clarified (Section 4.4). The use of GPM half-hour rainfall data introduces temporal and spatial discrepancies with SAR imaging, which should be acknowledged. Figure 16 illustrates a noticeable discrepancy in the center location of the rainfall. It is recommended to define the criteria for identifying rain cells and directly compare them with ground truth rather than relying on GPM data.
Others
1) Geographical Coordinates and Imaging Time: Remote sensing images should include geographical coordinates and imaging time, which are crucial in geoscience research.
2) Terminology and Labeling: On line 289, page, ‘individual’ might not be accurate. It is a group approaching the shore (Figure 13b).
3) The abbreviation IW is ambiguous and can refer to both Sentinel-1 imaging mode and internal waves.
4) The term BG in the figures is not explained in the text.
5) Units and numbers should have a space in between (e.g., lines 348 and 349, page 14).
This manuscript presents valuable work on SAR image segmentation of oceanic and atmospheric phenomena. Addressing the issues mentioned above will significantly enhance the manuscript’s clarity, rigor, and impact. I look forward to seeing the revised version.
Citation: https://doi.org/10.5194/essd-2024-222-RC1
Data sets
A dataset for semantic segmentation of typical oceanic and atmospheric phenomena from Sentinel-1 images Quankun Li, Xue Bai, and Xupu Geng https://doi.org/10.5281/zenodo.11410662
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