Zhang Hankui

This dataset includes (i) the image patches dataset and (ii) application/prediction (not training) codes for Landsat 8 cloud and cloud shadow masking used in a paper in review and uploaded here: Hankui Zhang, Dong Luo, David Roy, A learning attention network algorithm (LANA) for accurate Landsat-8 cloud and shadow masking, Remote Sensing of Environment The documentation is in l8_training_data_readme_new.pdf.

10.5281/zenodo.7786455

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Land Cover result (2011) from 1d-CNN on CONUS

Zenodo (CERN European Organization for Nuclear Research) (2023)

Zhang Hankui

The document is the land cover results from the paper: Hankui K. Zhang, David P. Roy, Dong Luo, Demonstration of large area land cover classification with a one dimensional convolutional neural network applied to single pixel temporal metric percentiles. Remote sensing of environment. (revision) There is total 482 files and the name of each file is: Landsatard2011.hxxvxx.7p.cnnnlcd.tif The “hxx” and “vxx” are the unique horizontal and vertical number that aligning with the Conterminous U.S. Landsat Analysis Ready Data (ARD) Tiles https://www.usgs.gov/media/images/conterminous-us-landsat-analysis-ready-data-ard-tiles. Total 15 classes: 11: Open water 21: Developed, open-space 22: Developed, low-intensity 23: Developed, medium-intensity 24: Developed, high-intensity 31: Barren land 41: Deciduous forest 42: Evergreen forest 43: Mixed forest 52: Shrub/scrub 71: Grassland/herbaceous 81: Pasture/hay 82: Cultivated crops 90: Woody wetlands 95: Emergent herbaceous wetland The data format is GEOTIFF files with unit8 data type. It has "cnn1d_landcover_result_readme" and zip file "cnn1d.7p.result.u8".

Conus

Land Cover

10.5281/zenodo.7789405

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Training patches and prediction codes of deep learning (LANA) model for Landsat 8/9 cloud/shadow mask

Zenodo (CERN European Organization for Nuclear Research) (2023)

Dong Luo Zhang Hankui

This dataset includes (i) the image patches dataset and (ii) application/prediction (not training) codes for Landsat 8 cloud and cloud shadow masking used in a paper in review and uploaded here: Hankui Zhang, Dong Luo, David Roy, A learning attention network algorithm (LANA) for accurate Landsat-8 cloud and shadow masking, Remote Sensing of Environment The documentation is in l8_training_data_readme_new.pdf.

10.5281/zenodo.7865321

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Research on Extracting Directly and Indirectly Copper Mineral Information Using Hyperion Data

Zhang Hankui Dengrong Zhang Zhu Jun Le Yu

This paper aims to extracting the copper mine information using Hyperion data from areas with different land cover condition.The L1 level Hyperion data is first preprocessed including band selection,destriping,atmosphere correction and geometric correction.Then different information extraction methods were adopted according to the different vegetation cover conditions.Spectral angle mapping(SAM) method is applied directly in the open canopy area.In vegetated area,we used a vegetation physiology abnormal indicator,which is calculated by hyperspectral vegetation index,to indicate accumulations of copper ion,then to identify copper mine indirectly.The experimental result shows that using complementary information from these two methods synthetically could benefit to extract the copper mine information in the area even covered by complex vegetation distribution.

Vegetation Cover

Copper mine

10.11873/j.issn.1004-0323.2010.6.881

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Landsat time series classification training data

Zenodo (CERN European Organization for Nuclear Research) (2023)

Zhang Hankui

Data for the paper Hankui K. Zhang, Dong Luo, Zhongbin Li, Classifying raw irregular Landsat time series (CRIT) for large area land cover mapping by adapting Transformer model. It stores the daily raw Landsat ARD annual good quality surface reflectance time series for 1985, 2006 and 2018 for CONUS with 7 land cover classes. Details are in the paper.

10.5281/zenodo.8097696

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Landsat time series classification training data

Zenodo (CERN European Organization for Nuclear Research) (2023)

Zhang Hankui

Data for the paper Hankui K. Zhang, Dong Luo, Zhongbin Li, Classifying raw irregular Landsat time series (CRIT) for large area land cover mapping by adapting Transformer model. It stores the daily raw Landsat ARD annual good quality surface reflectance time series for 1985, 2006 and 2018 for CONUS with 7 land cover classes. Details are in the paper.

10.5281/zenodo.8097697

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Remote sensing estimation of forest canopy density combined with texture features.

Linye Kexue (2012)

Yang Wu Dengrong Zhang Zhang Hankui WU Hong-gan

Texture (cosmology)

Tree canopy

Density estimation

Source

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PETROLEUM REMOTE SENSING DURING THE LAST TWENTY YEARS

Guotu ziyuan yaogan (2011)

Dengrong Zhang Zhang Hankui Le Yu Qian Chen Feng Xin-qiang

10.6046/gtzyyg.1999.03.05

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comment on Zhang et al. 2020

Zhang Hankui

Zhàng

10.5194/essd-2020-182-rc1

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Citations (8)

Land Cover result (2011) from 1d-CNN on CONUS

Zenodo (CERN European Organization for Nuclear Research) (2023)

Zhang Hankui

The document is the land cover results from the paper: Hankui K. Zhang, David P. Roy, Dong Luo, Demonstration of large area land cover classification with a one dimensional convolutional neural network applied to single pixel temporal metric percentiles. Remote sensing of environment. (revision) There is total 482 files and the name of each file is: Landsatard2011.hxxvxx.7p.cnnnlcd.tif The “hxx” and “vxx” are the unique horizontal and vertical number that aligning with the Conterminous U.S. Landsat Analysis Ready Data (ARD) Tiles https://www.usgs.gov/media/images/conterminous-us-landsat-analysis-ready-data-ard-tiles. Total 15 classes: 11: Open water 21: Developed, open-space 22: Developed, low-intensity 23: Developed, medium-intensity 24: Developed, high-intensity 31: Barren land 41: Deciduous forest 42: Evergreen forest 43: Mixed forest 52: Shrub/scrub 71: Grassland/herbaceous 81: Pasture/hay 82: Cultivated crops 90: Woody wetlands 95: Emergent herbaceous wetland The data format is GEOTIFF files with unit8 data type. It has "cnn1d_landcover_result_readme" and zip file "cnn1d.7p.result.u8".

Conus

10.5281/zenodo.7789404

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