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用于农田分类的融合双时相光学雷达数据集

用于农田分类的融合双时相光学雷达数据集

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Agriculture Classification

Data Set Information:This big data set is a fused bi-temporal optical-radar data for cropland classification. The images......

数据结构 ? 151.74M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Data Set Information:

    This big data set is a fused bi-temporal optical-radar data for cropland classification. The images were collected by RapidEye satellites (optical) and the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system (Radar) over an agricultural region near Winnipeg, Manitoba, Canada on 2012.
    There are 2 * 49 radar features and 2 * 38 optical features for two dates: 05 and 14 July 2012.
    Seven crop type classes exist for this data set as follows: 1-Corn; 2-Peas; 3- Canola; 4-Soybeans; 5- Oats; 6- Wheat; and 7-Broadleaf.


    Attribute Information:

    175 attributes including:
         1- class;
         2- f1 to f49:Polarimetric features on 05 July 2012;
         3- f50 to f98:Polarimetric features on 14 July 2012;
         4- f99 to f136:Optical features on 05 July 2012;
         5- f137 to f174:Optical features on 14 July 2012;

    Details:
    label:crop type class
    f1:sigHH_Rad05July
    f2:sigHV_Rad05July
    f3:sigVV_Rad05July
    f4:sigRR_Rad05July
    f5:sigRL_Rad05July
    f6:sigLL_Rad05July
    f7:Rhhvv_Rad05July
    f8:Rhvhh_Rad05July
    f9:Rhvvv_Rad05July
    f10:Rrrll_Rad05July
    f11:Rrlrr_Rad05July
    f12:Rrlll_Rad05July
    f13:Rhh_Rad05July
    f14:Rhv_Rad05July
    f15:Rvv_Rad05July
    f16:Rrr_Rad05July
    f17:Rrl_Rad05July
    f18:Rll_Rad05July
    f19:Ro12_Rad05July
    f20:Ro13_Rad05July
    f21:Ro23_Rad05July
    f22:Ro12cir_Rad05July
    f23:Ro13cir_Rad05July
    f24:Ro23cir_Rad05July
    f25:l1_Rad05July
    f26:l2_Rad05July
    f27:l3_Rad05July
    f28:H_Rad05July
    f29:A_Rad05July
    f30:a_Rad05July
    f31:HA_Rad05July
    f32:H1mA_Rad05July
    f33:1mHA_Rad05July
    f34:1mH1mA_Rad05July
    f35:PH_Rad05July
    f36:rvi_Rad05July
    f37:paulalpha_Rad05July
    f38:paulbeta_Rad05July
    f39:paulgamma_Rad05July
    f40:krogks_Rad05July
    f41:krogkd_Rad05July
    f42:krogkh_Rad05July
    f43:freeodd_Rad05July
    f44:freedbl_Rad05July
    f45:freevol_Rad05July
    f46:yamodd_Rad05July
    f47:yamdbl_Rad05July
    f48:yamhlx_Rad05July
    f49:yamvol_Rad05July
    f50:sigHH_Rad14July
    f51:sigHV_Rad14July
    f52:sigVV_Rad14July
    f53:sigRR_Rad14July
    f54:sigRL_Rad14July
    f55:sigLL_Rad14July
    f56:Rhhvv_Rad14July
    f57:Rhvhh_Rad14July
    f58:Rhvvv_Rad14July
    f59:Rrrll_Rad14July
    f60:Rrlrr_Rad14July
    f61:Rrlll_Rad14July
    f62:Rhh_Rad14July
    f63:Rhv_Rad14July
    f64:Rvv_Rad14July
    f65:Rrr_Rad14July
    f66:Rrl_Rad14July
    f67:Rll_Rad14July
    f68:Ro12_Rad14July
    f69:Ro13_Rad14July
    f70:Ro23_Rad14July
    f71:Ro12cir_Rad14July
    f72:Ro13cir_Rad14July
    f73:Ro23cir_Rad14July
    f74:l1_Rad14July
    f75:l2_Rad14July
    f76:l3_Rad14July
    f77:H_Rad14July
    f78:A_Rad14July
    f79:a_Rad14July
    f80:HA_Rad14July
    f81:H1mA_Rad14July
    f82:1mHA_Rad14July
    f83:1mH1mA_Rad14July
    f84:PH_Rad14July
    f85:rvi_Rad14July
    f86:paulalpha_Rad14July
    f87:paulbeta_Rad14July
    f88:paulgamma_Rad14July
    f89:krogks_Rad14July
    f90:krogkd_Rad14July
    f91:krogkh_Rad14July
    f92:freeodd_Rad14July
    f93:freedbl_Rad14July
    f94:freevol_Rad14July
    f95:yamodd_Rad14July
    f96:yamdbl_Rad14July
    f97:yamhlx_Rad14July
    f98:yamvol_Rad14July
    f99:B_Opt05July
    f100:G_Opt05July
    f101:R_Opt05July
    f102:Redge_Opt05July
    f103:NIR_Opt05July
    f104:NDVI_Opt05July
    f105:SR_Opt05July
    f106:RGRI_Opt05July
    f107:EVI_Opt05July
    f108:ARVI_Opt05July
    f109:SAVI_Opt05July
    f110:NDGI_Opt05July
    f111:gNDVI_Opt05July
    f112:MTVI2_Opt05July
    f113:NDVIre_Opt05July
    f114:SRre_Opt05July
    f115:NDGIre_Opt05July
    f116:RTVIcore_Opt05July
    f117:RNDVI_Opt05July
    f118:TCARI_Opt05July
    f119:TVI_Opt05July
    f120:PRI2_Opt05July
    f121:MeanPC1_Opt05July
    f122:VarPC1_Opt05July
    f123:HomPC1_Opt05July
    f124:ConPC1_Opt05July
    f125:DisPC1_Opt05July
    f126:EntPC1_Opt05July
    f127:SecMomPC1_Opt05July
    f128:CorPC1_Opt05July
    f129:MeanPC2_Opt05July
    f130:VarPC2_Opt05July
    f131:HomPC2_Opt05July
    f132:ConPC2_Opt05July
    f133:DisPC2_Opt05July
    f134:EntPC2_Opt05July
    f135:SecMomPC2_Opt05July
    f136:CorPC2_Opt05July
    f137:B_Opt14July
    f138:G_Opt14July
    f139:R_Opt14July
    f140:Redge_Opt14July
    f141:NIR_Opt14July
    f142:NDVI_Opt14July
    f143:SR_Opt14July
    f144:RGRI_Opt14July
    f145:EVI_Opt14July
    f146:ARVI_Opt14July
    f147:SAVI_Opt14July
    f148:NDGI_Opt14July
    f149:gNDVI_Opt14July
    f150:MTVI2_Opt14July
    f151:NDVIre_Opt14July
    f152:SRre_Opt14July
    f153:NDGIre_Opt14July
    f154:RTVIcore_Opt14July
    f155:RNDVI_Opt14July
    f156:TCARI_Opt14July
    f157:TVI_Opt14July
    f158:PRI2_Opt14July
    f159:MeanPC1_Opt14July
    f160:VarPC1_Opt14July
    f161:HomPC1_Opt14July
    f162:ConPC1_Opt14July
    f163:DisPC1_Opt14July
    f164:EntPC1_Opt14July
    f165:SecMomPC1_Opt14July
    f166:CorPC1_Opt14July
    f167:MeanPC2_Opt14July
    f168:VarPC2_Opt14July
    f169:HomPC2_Opt14July
    f170:ConPC2_Opt14July
    f171:DisPC2_Opt14July
    f172:EntPC2_Opt14July
    f173:SecMomPC2_Opt14July
    f174:CorPC2_Opt14July

    For more information about these attributes, please refer to relevant papers.


    Relevant Papers:

    1- Khosravi, I., & Alavipanah, S. K. (2019). A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations. International Journal of Remote Sensing, 40(18), 7221-7251.a€?
    2- Khosravi, I., et al. (2018). MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data. International Journal of Remote Sensing, 39(8), 2159-2176.a€?

    These papers can be downloaded from [Web link]


    Citation Request:

    I'd like to present my acknowledgment to the JPL NASA for the PolSAR images, and the SMAPVEX 2012 team, the Agriculture and Agri-Food Canada, for providing the PolSAR and the optical images.
    Please cite my relevant papers.


    Dr. Iman Khosravi,
    Postdoctoral researcher,
    Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran, Tehran, I.R. Iran, 1417853933
    E-Mail: iman.khosravi '@' ut.ac.ir
    Website: http://i-khosravi.ir

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