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  源代码及测试数据
  公开数据集
  综述和书籍代码与数据集
  学术链接
 

源代码及测试数据

 

 
  ●  LoveNAS (更新日期:2024.1.18).     Python Code
J. Wang, Y. Zhong, A. Ma, Z. Zheng, Y. Wan, L. Zhang. “LoveNAS: Towards Multi-Scene Land-Cover Mapping via Hierarchical Searching Adaptive Network”, in ISPRS Journal of Photogrammetry and Remote Sensing, 2024.
  ●  ScaleControlAgent (更新日期:2024.1.3).     Python Code
Y. Liu, Y. Zhong, S.Shi, L.Zhang. “Scale-aware Deep Reinforcement Learning for High Resolution Remote Sensing Imagery Classification”, in ISPRS Journal of Photogrammetry and Remote Sensing, 2024.
  ●  T-HOneCls(更新日期:2023.8.31).     Python Code
H. Zhao,X. Wang, J. Li, Y. Zhong. “Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery”, in IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
  ●  HOneCls(更新日期:2023.8.31).     Python Code
H. Zhao, Y. Zhong, X. Wang and H. Shu, "One-Class Risk Estimation for One-Class Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-17, 2023, Art no. 5518017, doi: 10.1109/TGRS.2023.3292929.
  ●  ITreeDet(更新日期:2023.8.31).     Python Code
H. Zhao, Y. Zhong, X. Wang, X. Hu, C. Luo, M. Boitt, R. Piiroinen, L. Zhang, J. Heiskanen, P. Pellikka. “Mapping the distribution of invasive tree species using deep one-class classification in the tropical montane landscape of Kenya”, in ISPRS journal of photogrammetry and remote sensing, vol 187, pp. 328-344, 2022, doi: https://doi.org/10.1016/j.isprsjprs.2022.03.005.
  ●  LoveCS(更新日期:2022.5.13).     More
J. Wang, A. Ma, Y. Zhong, Z. Zheng, L. Zhang. “Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery”, Remote Sensing of Environment, vol 277, pp. 113058, 2022, doi: https://doi.org/10.1016/j.rse.2022.113058.
  ●  Auto-AD(更新日期:2021.12.21).     Python Code
S. Wang, X. Wang, L. Zhang and Y. Zhong, "Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14, 2022, Art no. 5503314, doi: 10.1109/TGRS.2021.3057721.
  ●  LoveDA(更新日期:2021.10.21).     More
J. Wang, Z. Zheng, A. Ma, X. Lu, Y. Zhong. “LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation”, in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS), vol 1, 2021.1.
  ●  ChangeStar(更新日期:2021.8.31).     More
Z. Zheng, A. Ma, L. Zhang, Y. Zhong. “Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery”, in IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
  ●  FactSeg(更新日期:2021.7.18).     More
A. Ma, J. Wang, Y. Zhong, Z. Zheng. “FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery”, in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3097148 (Accepted).
  ●  SAED(更新日期:2021.6.26).     MATLAB code
X. Wang, Y. Zhong, C. Cui, L. Zhang and Y. Xu, "Autonomous Endmember Detection via an Abundance Anomaly Guided Saliency Prior for Hyperspectral Imagery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 2336-2351, March 2021, doi: 10.1109/TGRS.2020.3001353.
  ●  FPGA(更新日期:2020.5.28).     Python code
Z. Zheng, Y. Zhong, A. Ma, and L. Zhang, "FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5612-5626, Augest. 2020.
  ●  自适应多目标遥感影像聚类(更新日期:2016.4.21,2.0版).     code and data
A. Ma, Y. Zhong, and L. Zhang, "Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 8, pp. 4202-4217, 2015.
  ●  SGSNMF(更新日期:2018.5.4).     MATLAB code
X. Wang, Y. Zhong, L. Zhang and Y. Xu, "Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral unmixing," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp. 6287-6304, 2017.
  ●  SGIDN     Python code
Zhong, Y., W. Li, X. Wang, S. Jin and L. Zhang. "Satellite-ground integrated destriping network: A new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets." Remote Sensing of Environment237: 111416.
  ●  SSBFC     Python code
Zhao B , Zhong Y , Zhang L . A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2016, 116(Jun.):73-85.
  ●  DMTM     Python code
Zhao B , Zhong Y , Xia G S , et al. Dirichlet-derived multiple topic scene classification model fusing heterogeneous features for high resolution remote sensing imagery[J]. IEEE Transactions on Geoence and Remote Sensing, 2015, 54(4).
 
    公开数据集  

 
  ●   Hi-CNA dataset (Date:2024.3.18).    
   WThe Hi-CNA is a high-resolution remote sensing dataset dedicated to the cropland non-agriculturalization (CNA) tasks, featuring high-quality semantic and change annotations for cropland. The study area covers parts of Baoding, Xianyang, Xi'an, Zhongxiang, Tai'an, and Yanzhou in China, with a total area exceeding 1100 km2. These regions exhibit significant variations in crop planting, ensuring the diversity of cropland morphologies. The first temporal phase spans from 2015 to 2017, while the second phase ranges from 2020 to 2022, covering multiple phenological periods of crops. These characteristics provide a rich variety of samples for CNA tasks. The dataset is sourced from multispectral GF-2 fusion images with a spatial resolution of 0.8m, encompassing four bands including visible light and near-infrared. All images are cropped to 512*512, resulting in a total of 6797 pairs of dual-temporal images with corresponding annotations. Figure 1 illustrates different forms of cropland and some types of changes.
Fig. 1.The Hi-CNA dataset.(a) Study area. (b) Primary change tvpes, (c) Images and annotations of selected scenes in Hi-CNA

  ●   WUSU: Multi-temporal Wuhan urban semantic understanding dataset for classification and change detection based on GF-2 remote sensing imagery (Date:2024.1.3).     download
   WUSU (Wuhan Urban Semantic Understanding) dataset is collected and shared by the RSIDEA research group of Wuhan University, and it could serve as a benchmark dataset for multi-temporal classification and semantic change detection studies. WUSU focuses on urban structure and the urbanization process in Wuhan, the main city of the Yangtze River Economic Belt, and covers key development areas including Jiang'an District and Hongshan District, spanning a total geographic area of nearly 80km2 The dataset includes high resolution, tri-temporal, and multi-spectral satellite images of these districts, offering unprecedented detail and continuity in the representation of urban changes.
Fig. 1.WUSU dataset

  ●   CRLC: 10-meter resolution land cover maps for China in 2020 achieved by the deep classification network, the estimated overall accuracy is 84.35% ± 0.92% (Date:2023.4.18).     download
    Name: N_<lower left longitude>_<lower left latitude>.tif
    The maps include eight land cover classes: 1: Cropland, 2: Forest, 3: Grass/Shrubland, 5: Wetland, 6: Water bodies, 8: Impervious, 9: Bareland, 10: Snow/ice
Fig. 1.The case for CRLC land cover map

  ●   LoveDA dataset (Date:2022.1.13).     download
    1.5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan
    2.Focus on different geographical environments between Urban and Rural
    3.Advance both semantic segmentation and domain adaptation tasks
    4.Three considerable challenges:
        - Multi-scale objects
        - Complex background samples
        - Inconsistent class distributions.
Fig. 1.LoveDA dataset

  ●   WHU-Hi dataset for target detection (Date: 2021.12.21)     download
  ●   The Large-Scale Road Validation Dataset (Date: 2021.03.29)     download
    The large-scale road validation (LSRV) dataset was built for the validation of the road detection task, which contains the images from Boston and its surrounding cities in the United States, Birmingham in the United Kingdom and Shanghai in China.
image
mask
1 Boston and its surroundings, in the U.S Birmingham, in the UK Shanghai, in China
Fig. 1.The large-scale road validation dataset.

  ●   WHU-Hi数据集 (WHU-Hi dataset, 上传日期:2020.10.08).     download
   The Wuhan UAV-borne hyperspectral image (WHU-Hi) dataset was built for hyperspectral image classification and precise crops identification, which contains three individual UAV-borne hyperspectral datasets: WHU-Hi-LongKou, WHU-Hi-HanChuan, and WHU-Hi-HongHu. All the datasets were acquired in farming areas with various crop types in Hubei province, China, via a Headwall Nano-Hyperspec sensor mounted on a UAV platform. In addition, the WHU-Hi dataset could serve as a benchmark dataset for hyperspectral image classification studies.

Fig. 1.WHU-Hi dataset

  ●   SIRI-WHU谷歌影像数据集 (The Google image dataset of SIRI-WHU, 更新日期:2016.4.19).     download
该数据集包括12个类别,主要用于科研用途
以下各个类别中均包含200幅影像:
农场 商业区 港口 闲置用地
工业区 草地 立交桥 停车场
池塘 区民区 河流 水体
每一幅影像大小为200*200,空间分辨率为2米。
该数据集的获取来自谷歌地球,主要覆盖了中国的城市地区。
该数据集是由武汉大学的RS-IDEA研究组(SIRI-WHU)搜集制作。

当您发表的结果中用到了该数据集,请引用以下文献:
1. B. Zhao, Y. Zhong, G.-s. Xia, and L. Zhang, "Dirichlet-Derived Multiple Topic Scene Classification Model Fusing Heterogeneous Features for High Spatial Resolution Remote Sensing Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 4, pp. 2108-2123, Apr. 2016.
2. B. Zhao, Y. Zhong, L. Zhang, and B. Huang, "The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification," Remote Sensing, vol. 8, no. 2, p. 157, doi:10.3390/rs8020157 2016.
3. Q. Zhu, Y. Zhong, B. Zhao, G.-S. Xia, and L. Zhang, "Bag-of-Visual-Words Scene Classifier with Local and Global Features for High Spatial Resolution Remote Sensing Imagery," IEEE Geoscience and Remote Sensing Letters, DOI:10.1109/LGRS.2015.2513443 2016.
 
  综述和书籍代码与数据集  

 
       ●   综述代码与数据集     More
 
    学术链接  

 
●   Researchers
Liangpei Zhang Xuelong Li Dacheng Tao Jon Atli Benediktsson Jocelyn Chanussot Antonio Plaza Lorenzo Bruzzone
Paolo Gamba J. M. Bioucas-Dias Philip S. Yu Yew-Soon Ong Qian Du David M. Blei Michael Elad
Andrew Ng Aggelos K.Katsaggelos Chuanmin Hu Dar Roberts Feng Gao Thomas Hilker Jing.M.Chen 
Jon Atli Benediktsson Lei Zhang Li Xin Marvin Bauer Michael Ng Peyman Milanfar Qihao Weng
Ranga B.Myneni Roy David Shunlin Liang Stanley Osher Tim R.McVicar Truong Q. Nguyen Xiuping Jia

●   Journals
IEEE Transactions on Geoscience and Remote Sensing   Log In
ISPRS Journal Of Photogrammetry and Remote Sensing   Log In
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing   Log In
IEEE Geoscience and Remote Sensing Letters   Log In
International Journal of Remote Sensing   Log In
Remote Sensing Letters   Log In
Remote Sensing of Environment   Log In
International Journal of Digital Earth   Log In
International Journal of Applied Earth Observation and Geoinformation   Log In
Photogrammetric Engineering and Remote Sensing   Log In
Journal of Applied Remote Sensing   Log In
Pattern Recognition   Log In
IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics   Log In
IEEE Transactions on Image Processing   Log In
IEEE Transactions on Signal Processing   Log In
IEEE Journal of Selected Topics in Signal Processing   Log In
Neurocomputing   Log In
Mathematical Problems in Engineering   Log In

●   Others
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