Kuchinski, Vinícius

Paiva, Rodrigo Cauduro Dias de

English

Lume 5.8

Anais do XX SBSRXX SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTOXX Brazilian Symposium on Remote Sensing2 a 5 de abril de 2023Florianópolis, SCEditoresDouglas Francisco Marcolino GherardiIeda Del’Arco SanchesLuiz Eduardo Oliveira e Cruz de AragãoSão José dos Campos2023RECONSTRUCTION OF WATER MASK FROM OPTICAL CLOUDYLANDSAT SCENES USING NAIVE BAYES ALGORITHMVinícius Kuchinski1, Rodrigo Cauduro Dias de Paiva21PhD Candidate at IPH/UFRGS, eng.vinicius@outlook.com, 2IPH/UFRGS, rodrigocdpaiva@gmail.comABSTRACTFloods are natural events that can generate major disasters.Remote sensing data can be used for mapping flood events.However, atmospheric conditions in images captured byoptical sensors, such as the presence of clouds, affectthe recorded data and impair the use of these images inhydrological studies. In this context, a machine learningalgorithm – Naive Bayes – was used to determine the extentof water masks under two hypothetical cloud cover. Theresults, compared to the observed data, indicated that theproposed methodology could be used for the determinationof water masks, since the performance metrics reachedvalues for accuracy above 0.98 and for the critical successindex above 0.70. Further tests and validations should beperformed to prove the potential of reconstruction of watermasks by the proposed methodology.Key words – flood, inundation, machine learning, remotesensing1. INTRODUCTIONFlooding is a natural event being the most frequent type ofdisaster [1]. Some tools, such as maps, help the spatialrepresentation of floods [2] , however for the majority ofplaces there are no sufficient information to determine theextension of these events.Remote sensing is an important tool for mapping floodsbecause of the ability to observe water resources on largescale, with high temporal coverage and reasonable accuracy[3]. Optical sensors such as those embedded in Landsatsatellites have been proven capable of extracting water masksfrom flood events [4], [5]. However, despite the provenability, information obtained from these sensors are highlyaffected by atmospheric conditions such cloud presence orparticulate matter, for example [3].The objective of this work is to reconstruct water masksfrom images, whose data were obtained by optical sensorswith the presence of clouds, using the machine learningalgorithm Naive Bayes (NB). To achieve this, a flood eventwithout the presence of clouds was used as a benchmarkand two other images with the presence of clouds were usedto apply a cloud mask to the benchmark image. Finally,predicted values for the pixels under the cloud mask werecompared with the original values to verify the accuracy ofthe reconstruction.2. MATERIAL E METHODSThis work is part of a research in a larger context onflood mapping. Because of this, some previously performedFigure 1: Study area presenting the benchmark image (left) andthe water mask extracted by mNDWI (right).analyses will not be described in the methodology.2.1. Study areaThe study area of this work is a reach of Uruguai river (Figure1, left), located in the west portion of the Rio Grande doSul state, southern Brazil and South America. The Uruguairiver makes the border of two states of Brazil and also Brazil,Argentina and Uruguay.The region is hydrologically representative to southernBrazil, since the Uruguai river is the largest in extesion in theRio Grande do Sul state. In the river reach under analysis,due its topographic carachteristics, flood events have beenrecurrently recorded.2.2. Data usedFive features were used as input data to the model: digitalsurface model (DSM), land use and land cover (LULC),height above nearest drainage (HAND), frequency of floodoccurrence (FO) and discharge’s return period (QRP). Thesefeatures were selected because of their significance in thedetermination of flood inundation map by the NB model ina previously performed analysis.With the exception of QRP, the features were obtained fromthe Google Earth Engine (GEE) cloud computing platformby the application programming interface (API) available forGEE use in Python programming language. The data used areshown in Fig. 2Elevation is a basic factor that influences the intensity ofthe flood inundation [6]. For its representation was chosen thedigital surface model ALOS DSM [7], with spatial resolutionof approximately 30 meters.HAND normalizes the topography according to thedrainage network [8], representing in a complementary waythe distance of the analysis point to the drainage [9]. TheHAND selected is from MERIT Hydro [10], which is a globalhydrographic dataset with spatial resolution of 90 meters.https://proceedings.science/p/164182?lang=pt-br 426Figure 2: Data used as input to the NB model.LULC is an important factor to identify flood-susceptibleareas: vegetated areas tend to be less prone to flooding, unlikeurban and cropland areas [11]. The LULC data, producedby [12], are available in GEE with spatial resolution of 100meters.The frequency of flood occurrence over time (FO) is adataset with 30 meters of spatial resolution presented by Pekelet al. (2016). This feature was used to directly identify whichareas tend to be more flood prone.The return period of the discharge associated to the riverreach in that minibasin area (QPR), which comes from thehydrological and hydrodynamic model called Modelo deGrandes Bacias (MGB). The version of the model used is theMGB-AS, applied to all South America by [13]. The returnperiod was used to eliminate the differences in quantitiesbetween discharges of different minibasins.As observed data for the model, a water mask was used,which is a binary map resulting from a flood inventory, wherethe values indicate whether the pixel is considered not flooded(pixel=0) or flooded (pixel=1) [9]. The observed water maskwas extracted from an image of a reference event, withoutthe presence of clouds by applying the modified version ofnormalised difference water index (mNDWI) [14].Two other images of the same scene, but with the presenceof clouds in distinct coverage percentage were used to extracta cloud mask (by using the CloudMasking, a QGIS package).These cloud masks were applied to the reference water mask,excluding pixels that were under the cloud mask, creatinga hypothetical water mask that simulates the presence ofclouds. The hypothetical water masks remaining pixels wereconsidered the observed data for the models’ training.2.3. Naive Bayes modelDue to its simplicity and elegance, the NB algorithm forcategorical data was adopted. NB is the simplest Bayesiannetwork, based on Bayes’ theory (equation 1) [15].P (y|Feature) = P (y)× P (Feature|y)P (Feature)(1)where y is the class (not flooded/flooded), Feature is usedto describe the flood event, p(y/Feature) is the probabilityof the not flood/flood occur since the Feature occurs, p(y)and p(Feature) are the a priori probabilities.The NB algorithm is known as naive due the conditionalindependence, where all features are independent given aclass variable. When applying the algorithm to a real-worldproblem, this condition is difficult to be respected (JIANG etal., 2013). However, it has been proven that it is possible toobtain satisfactory results even if there is correlation betweenthe attributes [15].The NB classifier algorithm is a simplification of equation1. Considering an i number of features, the result of theclassification is ŷ, presented by the equation 2, adapted from[16]:ŷ = argmaxyp(y)n∏i=1p(Featurei|y) (2)where ŷ is the class determined by the algorithm, accordingto the highest value of the equation’s product, p(y) isthe a priori probability of the class (not flooded/flooded),p(Featurei/y) is the likelihood of Featurei depending onthe y class.Two models were trained, one for each dataset obtainedby applying the cloud masks. The models were conditionedusing the input features, with the water masks after theapplication of the cloud masks considered the observeddata. Validation occurred by predicting the flood extensionsthat were under the cloud mask, comparing them with theobserved pixels that had been excluded from the trainingdataset.2.4. Performance metricsTwo metrics were used to verify the accuracy of the modelsgenerated from the estimation of flood extents: accuracy(AC) and critical success index (CSI). Accuracy is definedby Equation 3, as per [17], and the CSI, which is also knownas fit [18] or threat score [19], is defined by Equation 4:AC =TP + TNTP + FP + TN + FN(3)CSI =TPTP + FP + FN(4)where: TP are the true positives, pixels that are predictedand observed flooded, TN are the true negatives, pixels thatare predicted and observed not flooded, FP are the falsepositives, pixels predicted flooded and observed not flooded,and FN are the false negatives, pixels predicted not floodedand observed flooded.AC was chosen because it is a metric that generallyrepresents the percentage of success on a predictive model,widely used in flood mapping works. Likewise, the choice forhttps://proceedings.science/p/164182?lang=pt-br 427Figure 3: The two images used to create the cloud mask (top)and the respective hypothetical water mask (bottom)CSI was due its metric that varies between 0 and 1, 0 meaningtotal disagreement between the observed and predictedflood extent, and 1 meaning total agreement. Therefore,it disregards true negatives, predicted and observed non-flooded, which is the value that causes accuracy imbalance,penalizing the result with false predictions (positive ornegative) [18].3. RESULTSThe flood event selected to be the benchmark is the one thatoccurred in January 4th, 2015. The image that represents thisevent is a Landsat 8, Collection 2, Level 2 located in path225, row 081. This event was selected due to the absence ofclouds (Figure 1, left). The water mask of this flood eventwas extracted using mNDWI and is showed in the right sideof Figure 1. This is the benchmark water mask.The images used to create the cloud mask are both fromthe same Landsat scene of the benchmark. Cloud masksextracted are from August 29, 2020 with 26% cloud coverand January 18, 2020 with 35% cloud cover, and they arepresented in the upper side of Figure 3. Both hypotheticallycreated water masks by applying these cloud masks to thebenchmark are presented in the lower side of Figure 3.Two NB models were trained with the input data and theremaining pixels of each hypothetical water mask used asobserved values. The prediction using the NB model trainedwith 26% cloud cover achieved an AC of 0.9817 and a CSI of0.706, considering only the pixels under the cloud mask. Forthe prediction using the NB with 35% cloud cover model, theperformance metrics were 0.9775 for AC and 0.713 for CSI.The results of the reconstructed pixels are showed in Figure4, left side for the reconstruction of the 26% cloud coverimage and 35% for the right side. More detailed results areshown in lower part of the Figure 4, visually comparing themodels performance.Figure 4: Reconstruction of pixels hypothetically considered ascloud. In the left the 26% and in the right the 35% cloud coverpredicted values under the cloud mask.4. DISCUSSIONA water mask from an image in which clouds wouldhypothetically be present was reconstructed using a NBmodel, estimating the lost information due the clouds basedon the remaining values, that were assimilated by the model.Although the temporal substitution method is the most usualfor reconstructing remote sensed images with the presence ofclouds [20], this is not applicable to the case, as a flood eventhas short duration and temporarily modifies the landscape,especially in the floodplain.A threshold of 0.65 for the CSI was proposed by [21] toconsider flood maps as locally relevant. The results obtainedby the methodology presented here achieved higher valuesof CSI than the proposed threshold. This could indicate thepossibility of using the NB model to reconstruct images foruse in studies on larger scales such as regional or continental,without prejudice to accuracy and using a flood event that,depending on the size of the cloud cover would not be used.5. CONCLUSIONSThis work explored the potential of the machine learningNaive Bayes algorithm to reconstruct water masks derivedfrom optical remote sensed images with presence of clouds.The NB model trained with the remaining data of a 26% ofcloud cover was able to reconstruct a water mask, extractedfrom a Landsat 8 image, with an AC of 0.9817 and a CSI of0.706. Another NB model, trained with 35% of cloud coverimage, extracted from the same Landsat scene, achieved theproposed performance metrics of 0.9817 and 0.706 for ACand CSI, respectively.https://proceedings.science/p/164182?lang=pt-br 428These results demonstrated the feasibility of using theproposed model in this context. For future work, arecommendation would be to investigate the use of the modelin other regions. In addition, in the same flood event, comparewater masks obtained by images captured by optical sensorswith masks obtained by microwave sensors such as thosepresent in Sentinel satellites.6. 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Reconstruction of water mask from optical cloudy Landsat scenes using Naive Bayes algorithm

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Reconstruction of water mask from optical cloudy Landsat scenes using Naive Bayes algorithm

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