基于许多不同类别的船舶/无船舶标记的卫星船舶图片数据集

该数据集提供了来自可见光谱的光学航拍图像的海上场景。MASATI 数据集包含动态海洋环境中的彩色图像，它可以用于评估船舶检测方法。每个图像可能包含一个或多个目标在不同的天气和光照条件下。数据集由 6212 个根据以下七类标记的卫星图像：陆地、海岸、海洋、船，多，海岸船和细节。 Main class Sub-class #samples Description ---------- --------- -------- ----------- Ship Ship 1015 Sea with a ship (no coast). Detail 1789 Ship details. Multi 188 Multiple ships. Coast & ship 121 Coast with ships. Non-ship Sea 1010 Sea (no ships). Coast 1054 Coast (no ships). Land 1035 Land (no sea). For evaluation we have defined three additional sets by grouping samples of several classes as follows: - Set 1: Ship on high sea and ocean or high sea without ship. - Set 2: Set 1 plus two new subsets: ship on sea close to coast (then coast is visible), and coast (sea scene with coast visible but without ship). - Set 3: Set 2 plus three new subsets: ship image acquired at lower altitude compared with the set 1, land (inland this is without coastal areas), and multi (multiple instances of ships). We plan to continuously collect and upload new marine scenes. As researchers use the data, we will list results and benchmarks here. If you have any results on the data that you would like to be listed here, please contact us (jgallego AT ua DOT es) The dataset has been compiled between March and September of 2016 from different regions in Europe, Africa, Asia, the Mediterranean sea and the Atlantic and Pacific oceans. LICENSE AND ACCESS ------------------------------------------- This dataset is shared only for non-profit research or educational purposes. If you use this dataset or a part of it, please respect these terms of use and reference the original work in which it was published. All data were obtained from Microsoft® Bing™ Maps. You can consult the Bing Maps terms of use at https://www.microsoft.com/maps/product/terms.html. Please read carefully the included file with the terms of use shown in Microsoft® Bing™ Maps. FILE FORMAT ------------------------------------------- The satellite images were acquired from Bing Maps in RGB and with different sizes, as size is dependent on the region of interest to be registered in the image. In general, the average image size has a spatial resolution around 512 x 512 pixels. The images are stored as PNG where pixel values represent RGB colors. The distance between targets and the acquisition satellite has also been changed in order to obtain captures at different altitudes. RELATED PUBLICATIONS (CITATION) ------------------------------------------- Please, if you use this dataset or part of it, cite the following publication: @article{Gallego2017, author = {Antonio-Javier Gallego, Antonio Pertusa, and Pablo Gil}, title = {Automatic Ship Detection from Optical Aerial Images with Convolutional Neural Networks}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, year = {2017}, }

本数据集提供可见光谱下的光学航拍海上场景图像。MASATI数据集涵盖动态海洋环境中的彩色图像，可用于船舶检测算法的性能评估。所有图像均拍摄于不同天气与光照条件下，且可能包含一个或多个目标物体。数据集共包含6212幅卫星图像，按以下7个类别标注：
1. 船舶（Ship）：含船舶且无海岸的远海海域，共1015幅
2. 船舶细节（Detail）：船舶局部细节图像，共1789幅
3. 多船（Multi）：包含多艘船舶的图像，共188幅
4. 海岸带船舶（Coast & ship）：邻近海岸且可见海岸的含船舶海域，共121幅
5. 纯海域（Non-ship Sea）：无船舶的开阔海域，共1010幅
6. 纯海岸（Coast）：无海域的海岸区域，共1054幅
7. 纯陆地（Land）：无海域的内陆区域，共1035幅

为开展模型评估，我们按如下方式将部分类别样本分组，构建了3个额外评估集：
- 评估集1（Set 1）：远海航行船舶或无船舶的远海海域
- 评估集2（Set 2）：在评估集1基础上新增两个子集：邻近海岸的含船舶海域（可见海岸），以及可见海岸但无船舶的海岸带海域
- 评估集3（Set 3）：在评估集2基础上新增三个子集：相较于评估集1拍摄高度更低的船舶图像、无海岸区域的内陆陆地，以及多船图像

我们计划持续收集并上传新的海上场景图像。随着研究者使用本数据集，我们将在此处更新相关实验结果与基准测试数据。若您有基于本数据集的研究成果希望在此展示，请联系我们（邮箱：jgallego AT ua DOT es，实际使用时请将AT替换为@、DOT替换为.）。

本数据集于2016年3月至9月期间采集自欧洲、非洲、亚洲、地中海、大西洋及太平洋的不同区域。

## 许可与访问协议
本数据集仅可用于非盈利性科研或教育用途。若您使用本数据集或其部分内容，请遵守本使用条款，并引用其发表的原始文献。所有数据均源自Microsoft® Bing™ Maps，您可通过https://www.microsoft.com/maps/product/terms.html查阅必应地图的使用条款。请仔细阅读数据集附带的Microsoft® Bing™ Maps使用条款文件。

## 文件格式
本数据集的卫星图像均从必应地图获取，采用RGB色彩空间，图像尺寸因感兴趣区域而异，整体平均空间分辨率约为512×512像素。图像以PNG格式存储，像素值对应RGB色彩分量。为获取不同拍摄高度的图像，我们调整了目标物体与拍摄卫星之间的距离。

## 相关出版物（引用格式）
若您使用本数据集或其部分内容，请引用以下文献：
@article{Gallego2017, author = {Antonio-Javier Gallego, Antonio Pertusa, and Pablo Gil}, title = {Automatic Ship Detection from Optical Aerial Images with Convolutional Neural Networks}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, year = {2017}

搜集汇总

背景与挑战

背景概述

该数据集是一个包含6212个标记卫星图像的数据集，主要用于船舶检测方法的评估。图像分为七类，涵盖不同地理位置和天气条件，平均分辨率为512x512像素，格式为PNG。