Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 MNDWI images of coasts.

<strong>Doodleverse/Segmentation Zoo Res-UNet models for identifying water in Sentinel-2 MNDWI images of coasts.</strong> <strong>Based on SWED*** data</strong> https://openmldata.ukho.gov.uk/ These Residual-UNet model data are based on images of coasts and associated labels. Models have been fitted to the following types of data 1. MNDWI (1 band): mndwi MNDWI = (Green - SWIR) / (Green + SWIR)<br> Green = pixel values from the green band<br> SWIR = pixel values from the short-wave infrared band Reference: Xu, H. "Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery." International Journal of Remote Sensing 27, No. 14 (2006): 3025-3033." (ESRI, 2018) Classes are: {0: null, 1: water}. These files are used in conjunction with Segmentation Zoo* For each model, there are 3 files with the same root name: 1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym** to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym** function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym** function `seg_images_in_folder.py` or the Segmentation Zoo* function `select_model_and_batch_process_folder.py` to segment a folder of images 3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model References * https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584<strong>Doodleverse/Segmentation Zoo 系列残差UNet（Residual UNet, Res-UNet）模型，用于识别海岸区域哨兵二号卫星（Sentinel-2）影像的归一化差异水体指数（Normalized Difference Water Index, MNDWI）波段中的水体。</strong> <strong>基于SWED***数据集构建</strong> 数据源链接：https://openmldata.ukho.gov.uk/ 本残差UNet模型数据集基于海岸区域影像及对应标注标签构建。模型适配以下类型的输入数据：1. 归一化差异水体指数（Normalized Difference Water Index, MNDWI）单波段数据：其计算公式为 MNDWI = (Green - SWIR) / (Green + SWIR)<br> Green = 绿光波段的像素值<br> SWIR = 短波红外波段的像素值 参考文献：Xu H. 《改进归一化差异水体指数以增强遥感影像中的开阔水体特征》，《国际遥感学报》，2006年，第27卷第14期：3025-3033。（ESRI，2018） 类别映射为：{0: 空类，1: 水体}。本数据集文件需配合Segmentation Zoo*工具使用。每个模型对应三个同名根文件名的文件，分别为：1. <strong>".json" 格式配置文件</strong>：该文件由Segmentation Gym**工具用于生成权重文件，其中包含模型构建方法、所用数据集信息，以及模型推理使用指南。该文件简洁实用，掌握该文件即可完全通晓整个Doodleverse工具链。2. <strong>".h5" 格式权重文件</strong>：该文件由Segmentation Gym**工具的`train_model.py`脚本生成，存储训练完成的模型参数权重。可通过Segmentation Gym**的`seg_images_in_folder.py`脚本，或Segmentation Zoo*的`select_model_and_batch_process_folder.py`脚本调用，实现对指定文件夹内影像的语义分割任务。3. <strong>"_modelcard.json" 格式模型卡片文件</strong>：该JSON文件包含用于完整描述模型起源、训练配置及所属数据集的字段信息。该文件与前文所述的`config`配置文件存在部分冗余内容，二者均包含模型训练与部署的相关说明。模型卡片文件不会被程序直接调用，但属于重要的元数据文件，需与其他模型相关文件一同留存，因此被视为模型套件的组成部分。相关参考链接：* https://github.com/Doodleverse/segmentation_zoo ** https://github.com/Doodleverse/segmentation_gym *** https://www.sciencedirect.com/science/article/abs/pii/S0034425722001584