Google Earth Engine – Applying Thresholds in Image Segmentation Using JavaScript

google-earth-engineimage segmentationjavascript

I am trying to segment the images based on the threshold values. For instance, I want to put a threshold range between > 800 and < 1200, but with my existing code in Google Earth Engine, I can put the only one function at a time that is greater than, below is my code, how to define greater than and less than at the same time.

 // Threshold the thermal band to set hot pixels as value 1, mask all else.
var hotspots = s2a.gt(3500) // i want put here both grater than and less than function
  .selfMask()
  .rename('hotspots');

// Display the thermal hotspots on the Map.
Map.addLayer(hotspots, {palette: 'FF0000'}, 'Hotspots');

Best Answer

Use the updateMask() function:

var hotspots = s2a.updateMask(s2a.gt(800)).updateMask(s2a.lt(1200))
        // set all values to 1 (instead of keeping the original values)
        .gt(800).selfMask()
        // rename the band
        .rename('hotspots');

Use the right parameters and always check metadata

You're trying to filter with BQA band and in SR product, there is no BQA band, there is pixel_qa band instead. Also, bits used are different (3 and 5). Finally, if you are trying to create a cloud-free composite, first sort by cloud cover.

var point = /* color: #98ff00 */ee.Geometry.Point([35.83946228027344, -3.5380934964711126]);

//load images for composite
var sr14= ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
.filterBounds(point)
.filterDate('2014-01-01','2014-03-01').sort('CLOUD_COVER',false);

// Temporally composite the images with a maximum value function.
var visParams = {bands: ['B4', 'B3', 'B2'], min:0,max: 1000};
Map.setCenter(35.8244, -3.5799);
Map.addLayer(sr14, visParams, 'max value composite');

var getQABits = function(image, start, end, newName) {
    // Compute the bits we need to extract.
    var pattern = 0;
    for (var i = start; i <= end; i++) {
       pattern += Math.pow(2, i);
    }
    // Return a single band image of the extracted QA bits, giving the band
    // a new name.
    return image.select([0], [newName])
                  .bitwiseAnd(pattern)
                  .rightShift(start);
};

// A function to mask out cloudy pixels.
var cloud_shadows = function(image) {
  // Select the QA band.
  var QA = image.select(['pixel_qa']);
  // Get the internal_cloud_algorithm_flag bit.
  return getQABits(QA, 3,3, 'cloud_shadows').eq(0);
  // Return an image masking out cloudy areas.
};

// A function to mask out cloudy pixels.
var clouds = function(image) {
  // Select the QA band.
  var QA = image.select(['pixel_qa']);
  // Get the internal_cloud_algorithm_flag bit.
  return getQABits(QA, 5,5, 'Cloud').eq(0);
  // Return an image masking out cloudy areas.
};

var maskClouds = function(image) {
  var cs = cloud_shadows(image);
  var c = clouds(image);
  image = image.updateMask(cs);
  return image.updateMask(c);
};

var composite_free = sr14.map(maskClouds);

Map.addLayer(composite_free,visParams, 'composite collection without clouds');

Link: https://code.earthengine.google.com/6a9d2bce417076d1f1b677e775584690

Google Earth Engine – Effective Image Segmentation Solutions

SNIC is just an algorithm, so it can be applied to the layer you want to segment. The "Crop Data Layer" is just an example layer.

I give you a simple example:

// site
var p = ee.Geometry.Point([-62.44, -38.40])
var site = p.buffer(10000)
// Sentinel 2 collection filtered
var s2 = ee.ImageCollection('COPERNICUS/S2')
           .filterBounds(p)
           .filterMetadata('CLOUD_COVERAGE_ASSESSMENT', 'less_than', 20)
// get the first image
var image = ee.Image(s2.first()).clip(site)    
// visualize the image
var vis = {bands:['B8', 'B11', 'B4'], min:0, max:5000}
Map.addLayer(image, vis, 'Image Sentinel 2')    
// segmentation with size 30
var snic = ee.Algorithms.Image.Segmentation.SNIC(image, 30)
// watch results of segmentation
Map.addLayer(snic.randomVisualizer(), null, 'SNIC')

Best Answer

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Google Earth Engine – Cloud Masking for Landsat 8 on Google Earth Engine

Use the right parameters and always check metadata

Google Earth Engine – Effective Image Segmentation Solutions

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