Crop mapping using fused optical-radar data set
Donated on 6/15/2020
Combining optical and PolSAR remote sensing images offers a complementary data set with a significant number of temporal, spectral, textural, and polarimetric features for cropland classification.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Other
Associated Tasks
Classification
Feature Type
Real
# Instances
325834
# Features
175
Dataset Information
Additional 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.
Has Missing Values?
No
Variable 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.
Dataset Files
| File | Size |
|---|---|
| WinnipegDataset.txt | 411.1 MB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset crop_mapping_using_fused_optical_radar_data_set = fetch_ucirepo(id=525) # data (as pandas dataframes) X = crop_mapping_using_fused_optical_radar_data_set.data.features y = crop_mapping_using_fused_optical_radar_data_set.data.targets # metadata print(crop_mapping_using_fused_optical_radar_data_set.metadata) # variable information print(crop_mapping_using_fused_optical_radar_data_set.variables)
Crop mapping using fused optical-radar data set [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C5G89D.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.