MMShip： medium resolution multispectral satellite imagery ship dataset

CHEN Li; LI Linhan; WANG Shiyong; GAO Sili; YE Xiangzhou

doi:10.37188/OPE.20233113.1962

您当前的位置：

首页 >

文章列表页 >

MMShip： medium resolution multispectral satellite imagery ship dataset

Information Sciences | 更新时间：2026-09-12

- MMShip： medium resolution multispectral satellite imagery ship dataset
- Optics and Precision Engineering Vol. 31, Issue 13, Pages: 1962-1972(2023)
- 作者机构：
  
  1.中国科学院上海技术物理研究所，上海 200083
  2.中国科学院大学，北京 100049
  3.中国科学院红外探测与成像技术重点实验室，上海 200083
- 作者简介：
  
  E-mail： s_y_w@sina.com
- 基金信息：
- DOI：10.37188/OPE.20233113.1962
  CLC： TP79
- Received：06 September 2022，
  
  Revised：2022-10-11，
  
  Published：10 July 2023
- 稿件说明：
移动端阅览
陈丽,李临寒,王世勇等.MMShip：中分辨率多光谱卫星图像船舶数据集[J].光学精密工程,2023,31(13):1962-1972.

CHEN Li,LI Linhan,WANG Shiyong,et al.MMShip： medium resolution multispectral satellite imagery ship dataset[J].Optics and Precision Engineering,2023,31(13):1962-1972.
陈丽,李临寒,王世勇等.MMShip：中分辨率多光谱卫星图像船舶数据集[J].光学精密工程,2023,31(13):1962-1972. DOI： 10.37188/OPE.20233113.1962.

CHEN Li,LI Linhan,WANG Shiyong,et al.MMShip： medium resolution multispectral satellite imagery ship dataset[J].Optics and Precision Engineering,2023,31(13):1962-1972. DOI： 10.37188/OPE.20233113.1962.

摘要

针对现有遥感船舶数据集均为裁剪后的图像，用数据集训练的检测算法直接运用于卫星图像原始尺度时检测效果较差的问题，建立了可见光和近红外4个波段的多光谱卫星船舶数据集MMShip，数据集同时包含卫星图像的原始尺度数据和切割后的小尺度船舶数据。本数据集引入多波段信息，弥补现有数据集多为可见光图像，而可见光容易受到光照条件等影响的缺点。在全球海域内下载云量低于3的Sentinel-2卫星图像，进行大气校正后只选取10 m分辨率的红绿蓝和近红外4个波段，以景为单位筛选出包含有船舶的图像。把筛选后的图像按无重叠的方式切分为512×512，剔除其中不包含船舶目标的图像。然后，使用LabelImage软件对小尺度数据进行了水平框标注，再将标注数据反推至原始尺度得到原始尺度下的标注信息。最后，利用几种典型的检测算法在切割后的MMShip小尺度数据集上进行了可见光、近红外、多光谱对比实验。构建了一个涵盖不同场景的多光谱卫星船舶目标数据集，包含497景原始尺度标注数据和裁剪后的5 016组船舶目标图像。对比实验验证了近红外波段信息的补充有助于提高船舶目标检测算法的精度。多光谱船舶数据集MMShip可用于卫星图像尺度和普通图像尺度的多光谱船舶目标检测算法研究。

Abstract

Considering that the existing remote-sensing ship datasets consist entirely of cropped images， the detection effect of the detection algorithm trained on the datasets is poor when it is directly applied to satellite images of the original scale. In this study， a multispectral satellite ship dataset MMShip with four bands of visible and near-infrared （NIR） light was established. The dataset includes both the original-scale data of satellite images and cut small-scale ship data. Owing to the introduction of multi-band information， this dataset compensates for the shortcoming that most of the existing datasets contain visible images， which are easily affected by illumination conditions. Sentinel-2 satellite images with cloud cover of <3 in the oceans worldwide were downloaded. After atmospheric correction， only four bands—red， green， blue， and NIR—with a 10-m resolution were selected， and the images containing ships were screened by scene. Next， the screened images were divided into a size of 512 × 512 such that the divided images do not overlap， and the images that did not contain the ship target were eliminated. The LabelImage software was used to label the small-scale data with a horizontal frame， and then the labeled data were converted to the original scale to obtain the labeling information under the original scale. Finally， several typical detection algorithms were used to perform visible-light， near-infrared， and multispectral comparison experiments on the altered MMShip small-scale dataset. In this study， a multispectral satellite ship target dataset covering different scenes was constructed， which included 497 original scale-labeled data and 5 016 groups of cropped ship target images. The contrast experiment confirmed that the addition of near-infrared band information can increase the accuracy of the ship target detection algorithm. The developed multispectral ship dataset MMShip can be applied to research on algorithms for multispectral ship target detection at the satellite-image and ordinary-image scales.

关键词

Keywords

references

徐芳，刘晶红，孙辉，等 . 光学遥感图像海面船舶目标检测技术进展［J］. 光学精密工程， 2021 ， 29 （ 4 ）： 916 - 931 . doi: 10.37188/OPE.2020.0419 http://dx.doi.org/10.37188/OPE.2020.0419

XU F ， LIU J H ， SUN H ， et al . Research progress on vessel detection using optical remote sensing image ［J］. Opt. Precision Eng. ， 2021 ， 29 （ 4 ）： 916 - 931 . （in Chinese） . doi: 10.37188/OPE.2020.0419 http://dx.doi.org/10.37188/OPE.2020.0419

WANG Z Q ， ZHOU Y ， WANG F T ， et al . SDGH-net： ship detection in optical remote sensing images based on Gaussian heatmap regression ［J］. Remote Sensing ， 2021 ， 13 （ 3 ）： 499 . doi: 10.3390/rs13030499 http://dx.doi.org/10.3390/rs13030499

QIN H N ， LI Y S ， LEI J ， et al . A specially optimized one-stage network for object detection in remote sensing images ［J］. IEEE Geoscience and Remote Sensing Letters ， 2021 ， 18 （ 3 ）： 401 - 405 . doi: 10.1109/lgrs.2020.2975086 http://dx.doi.org/10.1109/lgrs.2020.2975086

JIN L ， LIU G D . An approach on image processing of deep learning based on improved SSD ［J］. Symmetry ， 2021 ， 13 （ 3 ）： 495 . doi: 10.3390/sym13030495 http://dx.doi.org/10.3390/sym13030495

LI H ， DENG L B ， YANG C ， et al . Enhanced YOLO v3 tiny network for real-time ship detection from visual image ［J］. IEEE Access ， 2021 ， 9 ： 16692 - 16706 . doi: 10.1109/access.2021.3053956 http://dx.doi.org/10.1109/access.2021.3053956

XIE X Y ， LI B ， WEI X X . Ship detection in multispectral satellite images under complex environment ［J］. Remote Sensing ， 2020 ， 12 （ 5 ）： 792 . doi: 10.3390/rs12050792 http://dx.doi.org/10.3390/rs12050792

LIN T Y ， MAIRE M ， BELONGIE S ， et al . Microsoft COCO ： Common Objects in Context ［M］. Computer Vision-ECCV 2014 . Cham ： Springer International Publishing ， 2014 ： 740 - 755 . doi: 10.1007/978-3-319-10602-1_48 http://dx.doi.org/10.1007/978-3-319-10602-1_48

EVERINGHAM M ， ESLAMI S M ， GOOL L ， et al . The pascal visual object classes challenge： a retrospective ［J］. International Journal of Computer Vision ， 2015 ， 111 （ 1 ）： 98 - 136 . doi: 10.1007/s11263-014-0733-5 http://dx.doi.org/10.1007/s11263-014-0733-5

XIA G S ， BAI X ， DING J ， et al . DOTA： a large-scale dataset for object detection in aerial images ［C］. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1823，2018 ， Salt Lake City， UT， USA . IEEE ， 2018 ： 3974 - 3983 . doi: 10.1109/cvpr.2018.00418 http://dx.doi.org/10.1109/cvpr.2018.00418

CHENG G ， HAN J W ， ZHOU P C ， et al . Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection ［J］. IEEE Transactions on Image Processing ， 2019 ， 28 （ 1 ）： 265 - 278 . doi: 10.1109/tip.2018.2867198 http://dx.doi.org/10.1109/tip.2018.2867198

LI K ， WAN G ， CHENG G ， et al . Object detection in optical remote sensing images： a survey and a new benchmark ［J］. ISPRS Journal of Photogrammetry and Remote Sensing ， 2020 ， 159 ： 296 - 307 . doi: 10.1016/j.isprsjprs.2019.11.023 http://dx.doi.org/10.1016/j.isprsjprs.2019.11.023

LIU Z K ， YUAN L ， WENG L B ， et al . A high resolution optical satellite image dataset for ship recognition and some new baselines ［C］. Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods. February 24 - 26 ， 2017 . Porto， Portugal. SCITEPRESS - Science and Technology Publications ， 2017 ： 324 - 331 .

CHEN K ， WU M ， LIU J ， et al . FGSD ： A Dataset for Fine-grained Ship Detection in High Resolution Satellite Images ［EB/OL］. 2020 ： arXiv ： 2003 . 06832 . https：//arxiv.org/abs/2003.06832 https://arxiv.org/abs/2003.06832 .

RAINEY K ， STASTNY J . Object recognition in ocean imagery using feature selection and compressive sensing ［C］. 2011 IEEE Applied Imagery Pattern Recognition Workshop （AIPR）. 1113，2011 ， Washington， DC， USA. IEEE ， 2012 ： 1 - 6 . doi: 10.1109/aipr.2011.6176352 http://dx.doi.org/10.1109/aipr.2011.6176352

ZHANG Z N ， ZHANG L ， WANG Y ， et al . ShipRSImageNet： a large-scale fine-grained dataset for ship detection in high-resolution optical remote sensing images ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 2021 ， 14 ： 8458 - 8472 . doi: 10.1109/jstars.2021.3104230 http://dx.doi.org/10.1109/jstars.2021.3104230

ZOU Z X ， SHI Z W . Random access memories： a new paradigm for target detection in high resolution aerial remote sensing images ［J］. IEEE Transactions on Image Processing ， 2018 ， 27 （ 3 ）： 1100 - 1111 . doi: 10.1109/tip.2017.2773199 http://dx.doi.org/10.1109/tip.2017.2773199

CHENG G ， HAN J W ， LU X Q . Remote sensing image scene classification： benchmark and state of the art ［J］. Proceedings of the IEEE ， 2017 ， 105 （ 10 ）： 1865 - 1883 . doi: 10.1109/jproc.2017.2675998 http://dx.doi.org/10.1109/jproc.2017.2675998

CHENG G ， XIE X X ， HAN J W ， et al . Remote sensing image scene classification meets deep learning： challenges， methods， benchmarks， and opportunities ［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 2020 ， 13 ： 3735 - 3756 . doi: 10.1109/jstars.2020.3005403 http://dx.doi.org/10.1109/jstars.2020.3005403

LAM D ， KUZMA R ， MCGEE K ， et al . xView ： Objects in Context in Overhead Imagery ［EB/O L］. 2018 ： arXiv ： 1802 . 07856 . https：//arxiv.org/abs/1802.07856 https://arxiv.org/abs/1802.07856 .

Airbus . Airbus Ship Detection Challenge ， 2018 . ［Z/OL］. Accessed： Dec . 1 ， 2020 . https：//www.kaggle.com/c/airbus-ship-detection/data https://www.kaggle.com/c/airbus-ship-detection/data .

GALLEGO A J ， PERTUSA A ， GIL P . Automatic ship classification from optical aerial images with convolutional neural networks ［J］. Remote Sensing ， 2018 ， 10 （ 4 ）： 511 . doi: 10.3390/rs10040511 http://dx.doi.org/10.3390/rs10040511

姚力波，张筱晗，吕亚飞，等 . FGSC-23：面向深度学习精细识别的高分辨率光学遥感图像舰船目标数据集［J］. 中国图象图形学报， 2021 ， 26 （ 10 ）： 2337 - 2345 .

YAO L B ， ZHANG X H ， LYU Y F ， et al . FGSC-23： a large-scale dataset of high-resolution optical remote sensing image for deep learning-based fine-grained ship recognition ［J］. Journal of Image and Graphics ， 2021 ， 26 （ 10 ）： 2337 - 2345 . （in Chinese）

LIU W ， ANGUELOV D ， ERHAN D ， et al . SSD ： Single Shot MultiBox Detector ［M］. Computer Vision-ECCV 2016 . Cham ： Springer International Publishing ， 2016 ： 21 - 37 . doi: 10.1007/978-3-319-46448-0_2 http://dx.doi.org/10.1007/978-3-319-46448-0_2

LIN T Y ， GOYAL P ， GIRSHICK R ， et al . Focal loss for dense object detection ［C］. 2017 IEEE International Conference on Computer Vision （ICCV）. 2229，2017 ， Venice， Italy. IEEE ， 2017 ： 2999 - 3007 . doi: 10.1109/iccv.2017.324 http://dx.doi.org/10.1109/iccv.2017.324

KONG T ， SUN F C ， LIU H P ， et al . FoveaBox： beyound anchor-based object detection ［J］. IEEE Transactions on Image Processing ， 2020 ， 29 ： 7389 - 7398 . doi: 10.1109/tip.2020.3002345 http://dx.doi.org/10.1109/tip.2020.3002345

TIAN Z ， SHEN C H ， CHEN H ， et al . FCOS： fully convolutional one-stage object detection ［C］. 2019 IEEE/CVF International Conference on Computer Vision （ICCV）. 272，2019 ， Seoul， Korea （South）. IEEE ， 2020 ： 9626 - 9635 . doi: 10.1109/iccv.2019.00972 http://dx.doi.org/10.1109/iccv.2019.00972

REN S Q ， HE K M ， GIRSHICK R ， et al . Faster R-CNN： towards real-time object detection with region proposal networks ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence ， 2017 ， 39 （ 6 ）： 1137 - 1149 . doi: 10.1109/tpami.2016.2577031 http://dx.doi.org/10.1109/tpami.2016.2577031

CAI Z W ， VASCONCELOS N . Cascade R-CNN： delving into high quality object detection ［C］. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1823，2018 ， Salt Lake City， UT， USA . IEEE ， 2018 ： 6154 - 6162 . doi: 10.1109/cvpr.2018.00644 http://dx.doi.org/10.1109/cvpr.2018.00644

CHEN K ， WANG J ， PANG J ， et al . MMDetection ： open MMLab detection toolbox and benchmark ［EB/OL］. 2019 ： arXiv ： 1906 . 07155 . https：//arxiv.org/abs/1906.07155 https://arxiv.org/abs/1906.07155 .

Views

2126

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Automatic extraction of red raspberry planting areas using time series multispectral images

Automatic null testing of high-order aspheric surfaces based on sensitivity matrix

Design and implementation of a model for the characteristics of ground-based space target telescope's all day time detection capability

Review of Mamba-based methods for remote sensing image super-resolution

Automatic measurement for buried nano-hole structures in photonic crystal lasers

Related Author

WANG Zhipeng

WANG Xiaofei

Related Institution

Electronic Engineering College， Heilongjiang University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰