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TGRS-HRRSD-Dataset: High Resolution Remote Sensing Detection (HRRSD)

I. NOTE: JPEG files are available on BaiduCloud, GoogleDrive, and an ipv6 site bt.byr.cn

  • HRRSD contains 21,761 images acquired from Google Earth and Baidu Map with the spatial resolution from 0.15-m to 1.2-m.

  • There are 55,740 object instances in HRRSD.

  • HRRSD contains 13 categories of RSI objects.

Moreover, this dataset is divided as several subsets, image numbers in each subset are 5401 for ‘train’, 5417 for ‘val’, and 10943 for ‘test’. And ‘train-val’ subset is a merge of ‘train’ and ‘val’.

II. Mean and Std

In most current object detection systems, means and std values of datasets are required.

You may refer to:

mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]

Moreover, you can compute the values by yourself with file OPT2017/avr_std_detection_sets.py:

$ cd your_HRRSD_path/OPT2017
$ python avr_std_detection_sets.py 500

III. Folders

Labels

  • /OPT2017/Annotations: *.xml
  • /OPT2017/labels: *.txt with the form of (class x y width height)

Images

  • /OPT2017/JPEGImages: *.jpg

Dataset Division

  • /OPT2017/ImageSets/Main: Division of the dataset.

IV. Statistics and Benchmark

Statistics

Label Name N_Train N_Val N_Trainval N_Test N_All Mean Resized Scale /pixel Resized Scale Std /pixel
1 ship 950 948 1898 1988 3886 167.44 110.37
2 bridge 1123 1121 2244 2326 4570 246.10 110.53
3 ground track field 859 856 1717 2017 3734 276.50 100.65
4 storage tank 1099 1092 2191 2215 4406 125.60 68.41
5 basketball court 923 920 1843 2033 3876 108.19 57.46
6 tennis court 1043 1040 2083 2212 4295 102.71 38.80
7 airplane 1226 1222 2448 2451 4899 113.21 67.98
8 baseball diamond 1007 1004 2011 2022 4033 231.61 117.85
9 harbor 967 964 1931 1953 3884 163.96 94.16
10 vehicle 1188 1186 2374 2382 4756 41.96 9.99
11 crossroad 903 901 1804 2219 4023 220.54 59.24
12 T junction 1066 1065 2131 2289 4420 198.71 54.88
13 parking lot 1241 1237 2478 2480 4958 122.85 54.45

In this table, N_* refers to numbers of objects. 'Train', 'Val', 'Test' are three subsets of the dataset. 'Mean Resized Scale' shows average scale of each category. 'Resized Scale Std' is the standard deviation of category scale.

Benchmark

Category YOLO-v2 /% Fast R-CNN /% Fast_R-CNN_r50 + GACL-Net /% Faster R-CNN /% Faster_R-CNN_r50 + GACL-Net /%
Airplane 84.6 83.3 85.1 90.8 90.8
Baseball Diamond 62.2 83.6 82.6 86.9 87.2
Basketball Court 41.3 36.7 42.1 47.9 49.7
Bridge 79.0 75.1 76.7 85.5 85.6
Crossroad 43.4 67.1 68.7 88.6 88.2
Ground Track Field 94.4 90.0 89.6 90.6 90.7
Harbor 74.4 76.0 78.4 89.4 89.7
Parking Lot 45.8 37.5 39.5 63.3 65.3
Ship 78.5 75.0 74.3 88.5 88.5
Storage Tank 72.4 79.8 80.4 88.7 89.2
T Junction 46.8 39.2 38.8 75.1 75.0
Tennis Court 67.6 75.0 77.0 80.7 80.8
Vehicle 65.1 46.1 50.7 84.0 86.9
Mean AP 65.8 66.5 68.0 81.5 82.1

GACL-Net \cite{lu2020gated} is a method proposed to improve object localization performance. Title of this paper is "Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection". The suffix "r50" denotes using 50 layer Resnet.

V. FAQ

If any question is met, please contanct me with the e-mail: [email protected].

Qestion 1: AP for the "T junction" class is always NAN or 0, why?

Anwser Q1: In some object detection frameworks, there may be a piece of code like "cls_names = lower( cls_names )". This will set class names to lower case, but class names in xml files contain "T junction" where "T" is uppercase. This actually will cause several problems. The solution is using debug sofwares to find the code of changing word cases and correct it. For the dataset, I won't change the "T junction" labels in xmls currently for lacking time.

VI. Citation

If you find HRRSD dataset useful in your research, please consider citing:

@article{zhang2019hierarchical,
  title={Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection},
  author={Zhang, Yuanlin and Yuan, Yuan and Feng, Yachuang and Lu, Xiaoqiang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={57},
  number={8},
  pages={5535--5548},
  year={2019},
  publisher={IEEE}
}

For more comparative experimental results, please refer to:

@article{lu2020gated,
  title={Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection},
  author={Lu, Xiaoqiang and Zhang, Yuanlin and Yuan, Yuan and Feng, Yachuang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={58},
  number={1},
  pages={179--192},
  year={2020},
  publisher={IEEE}
}

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