removed watermask operator and used the classifier directly (#2)

* removed watermask operator and used the classifier directly

* inlined watermask also for OLCI and improved S2

idepix-olci/pom.xml

@@ -89,6 +89,10 @@
  <artifactId>snap-raster</artifactId>
  <version>${snap.version}</version>
  </dependency>
+ <dependency>
+ <groupId>org.esa.snap</groupId>
+ <artifactId>snap-watermask</artifactId>
+ </dependency>
 
  <dependency>
  <groupId>org.esa.snap</groupId>

idepix-olci/src/main/java/org/esa/snap/idepix/olci/IdepixOlciClassificationOp.java

@@ -22,13 +22,18 @@
 import org.esa.snap.idepix.core.util.IdepixIO;
 import org.esa.snap.idepix.core.util.IdepixUtils;
 import org.esa.snap.idepix.core.util.SchillerNeuralNetWrapper;
+import org.esa.snap.watermask.operator.WatermaskClassifier;
 
 import java.awt.*;
 import java.io.IOException;
 import java.io.InputStream;
 import java.util.Calendar;
 import java.util.Map;
 
+import static org.esa.snap.idepix.core.IdepixConstants.LAND_WATER_MASK_RESOLUTION;
+import static org.esa.snap.idepix.core.IdepixConstants.OVERSAMPLING_FACTOR_X;
+import static org.esa.snap.idepix.core.IdepixConstants.OVERSAMPLING_FACTOR_Y;
+
 /**
  * OLCI pixel classification operator.
  * Processes both land and water, so we get rid of separate land/water merge operator.
@@ -80,9 +85,6 @@ public class IdepixOlciClassificationOp extends Operator {
  @SourceProduct(alias = "rhotoa")
  private Product rad2reflProduct;
 
- @SourceProduct(alias = "waterMask", optional = true)
- private Product waterMaskProduct;
-
  @SourceProduct(alias = "o2Corr", optional = true)
  private Product o2CorrProduct;
 
@@ -91,7 +93,6 @@ public class IdepixOlciClassificationOp extends Operator {
 
 
  private Band[] olciReflBands;
- private Band landWaterBand;
 
  private Band surface13Band;
  private Band trans13Band;
@@ -113,6 +114,7 @@ public class IdepixOlciClassificationOp extends Operator {
  private GeometryFactory gf;
  private Polygon arcticPolygon;
  private Polygon antarcticaPolygon;
+ private WatermaskClassifier watermaskClassifier;
 
 
  @Override
@@ -121,13 +123,18 @@ public void initialize() throws OperatorException {
  nnInterpreter = IdepixOlciCloudNNInterpreter.create();
  readSchillerNeuralNets();
  createTargetProduct();
+ if (useSrtmLandWaterMask) {
+ try {
+ watermaskClassifier = new WatermaskClassifier(LAND_WATER_MASK_RESOLUTION,
+ OVERSAMPLING_FACTOR_X,
+ OVERSAMPLING_FACTOR_Y);
+ } catch (IOException e) {
+ throw new OperatorException("Could not initialise SRTM land-water mask", e);
+ }
+ }
 
  initSeaIceClassifier();
 
- if (waterMaskProduct != null && useSrtmLandWaterMask) {
- landWaterBand = waterMaskProduct.getBand("land_water_fraction");
- }
-
  if (o2CorrProduct != null) {
  surface13Band = o2CorrProduct.getBand("surface_13");
  trans13Band = o2CorrProduct.getBand("trans_13");
@@ -188,10 +195,6 @@ private void createTargetProduct() throws OperatorException {
 
  @Override
  public void computeTileStack(Map<Band, Tile> targetTiles, Rectangle rectangle, ProgressMonitor pm) throws OperatorException {
- Tile waterFractionTile = null;
- if (landWaterBand != null) {
- waterFractionTile = getSourceTile(landWaterBand, rectangle);
- }
 
  Tile surface13Tile = null;
  Tile trans13Tile = null;
@@ -216,13 +219,15 @@ public void computeTileStack(Map<Band, Tile> targetTiles, Rectangle rectangle, P
  nnTargetTile = targetTiles.get(targetProduct.getBand(IdepixConstants.NN_OUTPUT_BAND_NAME));
  }
  try {
+ GeoCoding geoCoding = sourceProduct.getSceneGeoCoding();
  for (int y = rectangle.y; y < rectangle.y + rectangle.height; y++) {
  checkForCancellation();
  for (int x = rectangle.x; x < rectangle.x + rectangle.width; x++) {
  int waterFraction = -1;
- if (waterFractionTile != null) {
- waterFraction = waterFractionTile.getSampleInt(x, y);
+ if (useSrtmLandWaterMask) {
+ waterFraction = watermaskClassifier.getWaterMaskFraction(geoCoding, x, y);
  }
+
  initCloudFlag(olciQualityFlagTile, targetTiles.get(cloudFlagTargetBand), olciReflectanceTiles, y, x);
  final boolean isBright = olciQualityFlagTile.getSampleBit(x, y, IdepixOlciConstants.L1_F_BRIGHT);
  cloudFlagTargetTile.setSample(x, y, IdepixConstants.IDEPIX_BRIGHT, isBright);

idepix-olci/src/main/java/org/esa/snap/idepix/olci/IdepixOlciOp.java

@@ -121,7 +121,6 @@ public class IdepixOlciOp extends BasisOp {
  private Product rad2reflProduct;
  private Product ctpProduct;
  private Product o2CorrProduct;
- private Product waterMaskProduct;
 
  private Map<String, Product> classificationInputProducts;
  private Map<String, Object> classificationParameters;
@@ -235,13 +234,6 @@ private void preProcess() {
  outputCtp);
  }
 
- if (useSrtmLandWaterMask) {
- HashMap<String, Object> waterMaskParameters = new HashMap<>();
- waterMaskParameters.put("resolution", IdepixConstants.LAND_WATER_MASK_RESOLUTION);
- waterMaskParameters.put("subSamplingFactorX", IdepixConstants.OVERSAMPLING_FACTOR_X);
- waterMaskParameters.put("subSamplingFactorY", IdepixConstants.OVERSAMPLING_FACTOR_Y);
- waterMaskProduct = GPF.createProduct("LandWaterMask", waterMaskParameters, sourceProduct);
- }
  }
 
  private void setClassificationParameters() {
@@ -261,7 +253,6 @@ private void setClassificationInputProducts() {
  classificationInputProducts = new HashMap<>();
  classificationInputProducts.put("l1b", sourceProduct);
  classificationInputProducts.put("rhotoa", rad2reflProduct);
- classificationInputProducts.put("waterMask", waterMaskProduct);
  if (considerCloudsOverSnow) {
  classificationInputProducts.put("o2Corr", o2CorrProduct);
  }

idepix-s2msi/pom.xml

@@ -35,6 +35,10 @@
  </description>
 
  <dependencies>
+ <dependency>
+ <groupId>org.esa.snap</groupId>
+ <artifactId>idepix-core</artifactId>
+ </dependency>
  <dependency>
  <groupId>org.esa.snap</groupId>
  <artifactId>ceres-core</artifactId>

idepix-s2msi/src/main/java/org/esa/snap/idepix/s2msi/S2IdepixClassificationOp.java

@@ -1,8 +1,6 @@
 package org.esa.snap.idepix.s2msi;
 
 import com.bc.ceres.core.ProgressMonitor;
-import org.esa.snap.idepix.s2msi.util.S2IdepixConstants;
-import org.esa.snap.idepix.s2msi.util.S2IdepixUtils;
 import org.esa.snap.core.datamodel.Band;
 import org.esa.snap.core.datamodel.FlagCoding;
 import org.esa.snap.core.datamodel.GeoCoding;
@@ -21,11 +19,19 @@
 import org.esa.snap.core.gpf.annotations.TargetProduct;
 import org.esa.snap.core.util.ProductUtils;
 import org.esa.snap.dem.gpf.AddElevationOp;
+import org.esa.snap.idepix.s2msi.util.S2IdepixConstants;
+import org.esa.snap.idepix.s2msi.util.S2IdepixUtils;
+import org.esa.snap.watermask.operator.WatermaskClassifier;
 
 import java.awt.Color;
 import java.awt.Rectangle;
+import java.io.IOException;
 import java.util.Map;
 
+import static org.esa.snap.idepix.core.IdepixConstants.LAND_WATER_MASK_RESOLUTION;
+import static org.esa.snap.idepix.core.IdepixConstants.OVERSAMPLING_FACTOR_X;
+import static org.esa.snap.idepix.core.IdepixConstants.OVERSAMPLING_FACTOR_Y;
+
 /**
  * Sentinel-2 (MSI) pixel classification operator.
  *
@@ -42,6 +48,7 @@ public class S2IdepixClassificationOp extends Operator {
  public static final double DELTA_RHO_TOA_442_THRESHOLD = 0.03;
  public static final double RHO_TOA_442_THRESHOLD = 0.03;
 
+
  private static final float WATER_MASK_SOUTH_BOUND = -58.0f;
 // private static final String VALID_PIXEL_EXPRESSION = "detector_footprint_B1 " +
 // "and detector_footprint_B2 " +
@@ -145,8 +152,8 @@ public class S2IdepixClassificationOp extends Operator {
  @SourceProduct(alias = "l1c", description = "The MSI L1C source product.")
  Product sourceProduct;
 
- @SourceProduct(alias = "waterMask", optional = true)
- private Product waterMaskProduct;
+// @SourceProduct(alias = "waterMask", optional = true)
+// private Product waterMaskProduct;
 
  @TargetProduct(description = "The target product.")
  Product targetProduct;
@@ -180,6 +187,7 @@ public class S2IdepixClassificationOp extends Operator {
  Band ndwiBand;
 
  private Product elevationProduct;
+ private WatermaskClassifier watermaskClassifier;
 
 
 // public static final String NN_NAME = "20x4x2_1012.9.net"; // Landsat 'all' NN
@@ -197,13 +205,20 @@ public void initialize() throws OperatorException {
  Color.GREEN, 0.0);
  validPixelMask.setOwner(getSourceProduct());
 
+ boolean isHigherResolutionInput = sourceProduct.getBand("B2") != null
+ && sourceProduct.getBand("B2").getGeoCoding().getMapCRS().getName().toString().contains("UTM")
+ && sourceProduct.getBand("B2").getImageToModelTransform().getScaleX() < LAND_WATER_MASK_RESOLUTION;
+ try {
+ watermaskClassifier = new WatermaskClassifier(LAND_WATER_MASK_RESOLUTION,
+ isHigherResolutionInput ? 1 : OVERSAMPLING_FACTOR_X,
+ isHigherResolutionInput ? 1 : OVERSAMPLING_FACTOR_Y);
+ } catch (IOException e) {
+ throw new OperatorException("Could not initialise SRTM land-water mask", e);
+ }
+
 // readSchillerNeuralNets();
  createTargetProduct();
 
- if (waterMaskProduct != null) {
- landWaterBand = waterMaskProduct.getBand("land_water_fraction");
- }
-
  if (sourceProduct.containsBand(S2IdepixConstants.ELEVATION_BAND_NAME)) {
  elevationProduct = sourceProduct;
  } else {
@@ -226,12 +241,7 @@ public void computeTileStack(Map<Band, Tile> targetTiles, Rectangle rectangle, P
  s2ReflectanceTiles[i] = getSourceTile(s2MsiReflBands[i], rectangle);
  }
 
- Tile waterFractionTile = null;
- if (waterMaskProduct != null) {
- waterFractionTile = getSourceTile(landWaterBand, rectangle);
- }
 
- GeoPos geoPos = null;
  final Band cloudFlagTargetBand = targetProduct.getBand(S2IdepixUtils.IDEPIX_CLASSIF_FLAGS);
  final Tile cloudFlagTargetTile = targetTiles.get(cloudFlagTargetBand);
 
@@ -250,6 +260,7 @@ public void computeTileStack(Map<Band, Tile> targetTiles, Rectangle rectangle, P
  final Tile elevationTile = getSourceTile(elevationBand, rectangle);
  final Tile validPixelTile = getSourceTile(validPixelMask, rectangle);
 
+
  try {
  for (int y = rectangle.y; y < rectangle.y + rectangle.height; y++) {
  checkForCancellation();
@@ -268,9 +279,9 @@ public void computeTileStack(Map<Band, Tile> targetTiles, Rectangle rectangle, P
  // set up pixel properties for given instruments...
  S2IdepixAlgorithm s2MsiAlgorithm = createS2MsiAlgorithm(s2ReflectanceTiles,
  szaTile, vzaTile, saaTile, vaaTile,
- waterFractionTile,
  elevationTile,
  validPixelTile,
+ watermaskClassifier,
  s2MsiReflectance,
  y,
  x);
@@ -409,10 +420,9 @@ private void copyReflectancesForCloudShadow() {
 
  private S2IdepixAlgorithm createS2MsiAlgorithm(Tile[] s2MsiReflectanceTiles,
  Tile szaTile, Tile vzaTile, Tile saaTile, Tile vaaTile,
- Tile waterFractionTile,
  Tile elevationTile,
  Tile validPixelTile,
- float[] s2MsiReflectances,
+ WatermaskClassifier classifier, float[] s2MsiReflectances,
  int y,
  int x) {
  S2IdepixAlgorithm s2MsiAlgorithm = new S2IdepixAlgorithm();
@@ -422,11 +432,9 @@ private S2IdepixAlgorithm createS2MsiAlgorithm(Tile[] s2MsiReflectanceTiles,
  }
  s2MsiAlgorithm.setRefl(s2MsiReflectances);
 
- boolean isLand = false;
- if (waterMaskProduct != null) {
- final int waterFraction = waterFractionTile.getSampleInt(x, y);
- isLand = isLandPixel(x, y, waterFraction, s2MsiAlgorithm);
- }
+ final byte waterFraction = classifier.getWaterMaskFraction(sourceProduct.getSceneGeoCoding(), x, y);
+
+ boolean isLand = isLandPixel(x, y, waterFraction, s2MsiAlgorithm);
  s2MsiAlgorithm.setIsLand(isLand);
 
  final double sza = szaTile.getSampleDouble(x, y);

idepix-s2msi/src/main/java/org/esa/snap/idepix/s2msi/S2IdepixOp.java

@@ -32,9 +32,6 @@
  description = "Pixel identification and classification for Sentinel-2 MSI.")
 public class S2IdepixOp extends Operator {
 
- private static final int LAND_WATER_MASK_RESOLUTION = 50;
- private static final int OVERSAMPLING_FACTOR_X = 3;
- private static final int OVERSAMPLING_FACTOR_Y = 3;
 
  @Parameter(defaultValue = "true",
  label = " Write TOA reflectances to the target product",
@@ -132,7 +129,6 @@ public class S2IdepixOp extends Operator {
  @TargetProduct(description = "The target product.")
  private Product targetProduct;
 
- private Product waterMaskProduct;
  private Product postProcessingProduct;
  private Product s2ClassifProduct;
 
@@ -149,11 +145,9 @@ public void initialize() throws OperatorException {
  }
 
  private void processSentinel2() {
- processLandWaterMask();
 
  Map<String, Product> inputProducts = new HashMap<>(4);
  inputProducts.put("l1c", sourceProduct);
- inputProducts.put("waterMask", waterMaskProduct);
 
  final Map<String, Object> pixelClassificationParameters = createPixelClassificationParameters();
 
@@ -181,23 +175,6 @@ private void processSentinel2() {
  setTargetProduct(targetProduct);
  }
 
- private void processLandWaterMask() {
- boolean isHigherResolutionInput = sourceProduct.getBand("B2") != null
- && sourceProduct.getBand("B2").getGeoCoding().getMapCRS().getName().toString().contains("UTM")
- && sourceProduct.getBand("B2").getImageToModelTransform().getScaleX() < LAND_WATER_MASK_RESOLUTION;
- HashMap<String, Object> waterMaskParameters = new HashMap<>();
- waterMaskParameters.put("resolution", LAND_WATER_MASK_RESOLUTION);
- if (isHigherResolutionInput) {
- System.out.println("No subsampling of " + sourceProduct.getBand("B2").getImageToModelTransform().getScaleX() + " m product necessary to access " + LAND_WATER_MASK_RESOLUTION + " m water mask");
- waterMaskParameters.put("subSamplingFactorX", 1);
- waterMaskParameters.put("subSamplingFactorY", 1);
- } else {
- waterMaskParameters.put("subSamplingFactorX", OVERSAMPLING_FACTOR_X);
- waterMaskParameters.put("subSamplingFactorY", OVERSAMPLING_FACTOR_Y);
- }
- waterMaskProduct = GPF.createProduct("LandWaterMask", waterMaskParameters, sourceProduct);
- }
-
  private void computePostProcessProduct() {
  postProcessingProduct = s2ClassifProduct;
  Product cloudBufferProduct = null;