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ee.Classifier.smileKNN

Creates an empty k-NN classifier.

The k-nearest neighbor algorithm (k-NN) is a method for classifying objects by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small, typically odd).

Usage	Returns
`ee.Classifier.smileKNN( k , searchMethod , metric )`	Classifier

Argument	Type	Details
`k`	Integer, default: 1	The number of neighbors for classification.
`searchMethod`	String, default: "AUTO"	Search method. The following are valid [AUTO, LINEAR_SEARCH, KD_TREE, COVER_TREE]. AUTO will choose between KD_TREE and COVER_TREE depending on the dimension count. Results may vary between the different search methods for distance ties and probability values. Since performance and results may vary consult with SMILE's documentation and other literature.
`metric`	String, default: "EUCLIDEAN"	The distance metric to use. NOTE: KD_TREE (and AUTO for low dimensions) will not use the metric selected. Options are: 'EUCLIDEAN' - Euclidean distance. 'MAHALANOBIS' - Mahalanobis distance. 'MANHATTAN' - Manhattan distance. 'BRAYCURTIS' - Bray-Curtis distance.

Examples

Code Editor (JavaScript)

 // Cloud masking for Landsat 8. 
 function 
  
 maskL8sr 
 ( 
 image 
 ) 
  
 { 
  
 var 
  
 qaMask 
  
 = 
  
 image 
 . 
 select 
 ( 
 'QA_PIXEL' 
 ). 
 bitwiseAnd 
 ( 
 parseInt 
 ( 
 '11111' 
 , 
  
 2 
 )). 
 eq 
 ( 
 0 
 ); 
  
 var 
  
 saturationMask 
  
 = 
  
 image 
 . 
 select 
 ( 
 'QA_RADSAT' 
 ). 
 eq 
 ( 
 0 
 ); 
  
 // Apply the scaling factors to the appropriate bands. 
  
 var 
  
 opticalBands 
  
 = 
  
 image 
 . 
 select 
 ( 
 'SR_B.' 
 ). 
 multiply 
 ( 
 0.0000275 
 ). 
 add 
 ( 
 - 
 0.2 
 ); 
  
 var 
  
 thermalBands 
  
 = 
  
 image 
 . 
 select 
 ( 
 'ST_B.*' 
 ). 
 multiply 
 ( 
 0.00341802 
 ). 
 add 
 ( 
 149.0 
 ); 
  
 // Replace the original bands with the scaled ones and apply the masks. 
  
 return 
  
 image 
 . 
 addBands 
 ( 
 opticalBands 
 , 
  
 null 
 , 
  
 true 
 ) 
  
 . 
 addBands 
 ( 
 thermalBands 
 , 
  
 null 
 , 
  
 true 
 ) 
  
 . 
 updateMask 
 ( 
 qaMask 
 ) 
  
 . 
 updateMask 
 ( 
 saturationMask 
 ); 
 } 
 // Map the function over one year of data. 
 var 
  
 collection 
  
 = 
  
 ee 
 . 
 ImageCollection 
 ( 
 'LANDSAT/LC08/C02/T1_L2' 
 ) 
  
 . 
 filterDate 
 ( 
 '2020-01-01' 
 , 
  
 '2021-01-01' 
 ) 
  
 . 
 map 
 ( 
 maskL8sr 
 ); 
 // Make a median composite. 
 var 
  
 composite 
  
 = 
  
 collection 
 . 
 median 
 (); 
 // Demonstration labels. 
 var 
  
 labels 
  
 = 
  
 ee 
 . 
 FeatureCollection 
 ( 
 'projects/google/demo_landcover_labels' 
 ) 
 // Use these bands for classification. 
 var 
  
 bands 
  
 = 
  
 [ 
 'SR_B2' 
 , 
  
 'SR_B3' 
 , 
  
 'SR_B4' 
 , 
  
 'SR_B5' 
 , 
  
 'SR_B6' 
 , 
  
 'SR_B7' 
 ]; 
 // The name of the property on the points storing the class label. 
 var 
  
 classProperty 
  
 = 
  
 'landcover' 
 ; 
 // Sample the composite to generate training data.  Note that the 
 // class label is stored in the 'landcover' property. 
 var 
  
 training 
  
 = 
  
 composite 
 . 
 select 
 ( 
 bands 
 ). 
 sampleRegions 
 ( 
  
 { 
 collection 
 : 
  
 labels 
 , 
  
 properties 
 : 
  
 [ 
 classProperty 
 ], 
  
 scale 
 : 
  
 30 
 }); 
 // Train a kNN classifier. 
 var 
  
 classifier 
  
 = 
  
 ee 
 . 
 Classifier 
 . 
 smileKNN 
 ( 
 5 
 ). 
 train 
 ({ 
  
 features 
 : 
  
 training 
 , 
  
 classProperty 
 : 
  
 classProperty 
 , 
 }); 
 // Classify the composite. 
 var 
  
 classified 
  
 = 
  
 composite 
 . 
 classify 
 ( 
 classifier 
 ); 
 Map 
 . 
 setCenter 
 ( 
 - 
 122.184 
 , 
  
 37.796 
 , 
  
 12 
 ); 
 Map 
 . 
 addLayer 
 ( 
 classified 
 , 
  
 { 
 min 
 : 
  
 0 
 , 
  
 max 
 : 
  
 2 
 , 
  
 palette 
 : 
  
 [ 
 'red' 
 , 
  
 'green' 
 , 
  
 'blue' 
 ]});

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

 import 
  
 ee 
 import 
  
 geemap.core 
  
 as 
  
 geemap

Colab (Python)

 # Cloud masking for Landsat 8. 
 def 
  
 mask_l8_sr 
 ( 
 image 
 ): 
 qa_mask 
 = 
 image 
 . 
 select 
 ( 
 'QA_PIXEL' 
 ) 
 . 
 bitwiseAnd 
 ( 
 int 
 ( 
 '11111' 
 , 
 2 
 )) 
 . 
 eq 
 ( 
 0 
 ) 
 saturation_mask 
 = 
 image 
 . 
 select 
 ( 
 'QA_RADSAT' 
 ) 
 . 
 eq 
 ( 
 0 
 ) 
 # Apply the scaling factors to the appropriate bands. 
 optical_bands 
 = 
 image 
 . 
 select 
 ( 
 'SR_B.' 
 ) 
 . 
 multiply 
 ( 
 0.0000275 
 ) 
 . 
 add 
 ( 
 - 
 0.2 
 ) 
 thermal_bands 
 = 
 image 
 . 
 select 
 ( 
 'ST_B.*' 
 ) 
 . 
 multiply 
 ( 
 0.00341802 
 ) 
 . 
 add 
 ( 
 149.0 
 ) 
 # Replace the original bands with the scaled ones and apply the masks. 
 return 
 ( 
 image 
 . 
 addBands 
 ( 
 optical_bands 
 , 
 None 
 , 
 True 
 ) 
 . 
 addBands 
 ( 
 thermal_bands 
 , 
 None 
 , 
 True 
 ) 
 . 
 updateMask 
 ( 
 qa_mask 
 ) 
 . 
 updateMask 
 ( 
 saturation_mask 
 ) 
 ) 
 # Map the function over one year of data. 
 collection 
 = 
 ( 
 ee 
 . 
 ImageCollection 
 ( 
 'LANDSAT/LC08/C02/T1_L2' 
 ) 
 . 
 filterDate 
 ( 
 '2020-01-01' 
 , 
 '2021-01-01' 
 ) 
 . 
 map 
 ( 
 mask_l8_sr 
 ) 
 ) 
 # Make a median composite. 
 composite 
 = 
 collection 
 . 
 median 
 () 
 # Demonstration labels. 
 labels 
 = 
 ee 
 . 
 FeatureCollection 
 ( 
 'projects/google/demo_landcover_labels' 
 ) 
 # Use these bands for classification. 
 bands 
 = 
 [ 
 'SR_B2' 
 , 
 'SR_B3' 
 , 
 'SR_B4' 
 , 
 'SR_B5' 
 , 
 'SR_B6' 
 , 
 'SR_B7' 
 ] 
 # The name of the property on the points storing the class label. 
 class_property 
 = 
 'landcover' 
 # Sample the composite to generate training data.  Note that the 
 # class label is stored in the 'landcover' property. 
 training 
 = 
 composite 
 . 
 select 
 ( 
 bands 
 ) 
 . 
 sampleRegions 
 ( 
 collection 
 = 
 labels 
 , 
 properties 
 = 
 [ 
 class_property 
 ], 
 scale 
 = 
 30 
 ) 
 # Train a kNN classifier. 
 classifier 
 = 
 ee 
 . 
 Classifier 
 . 
 smileKNN 
 ( 
 5 
 ) 
 . 
 train 
 ( 
 features 
 = 
 training 
 , 
 classProperty 
 = 
 class_property 
 ) 
 # Classify the composite. 
 classified 
 = 
 composite 
 . 
 classify 
 ( 
 classifier 
 ) 
 m 
 = 
 geemap 
 . 
 Map 
 () 
 m 
 . 
 set_center 
 ( 
 - 
 122.184 
 , 
 37.796 
 , 
 12 
 ) 
 m 
 . 
 add_layer 
 ( 
 classified 
 , 
 { 
 'min' 
 : 
 0 
 , 
 'max' 
 : 
 2 
 , 
 'palette' 
 : 
 [ 
 'red' 
 , 
 'green' 
 , 
 'blue' 
 ]} 
 ) 
 m

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Examples

Code Editor (JavaScript)

Colab (Python)

ee.Classifier.smileKNN