1. AlgorithminGeoInformatic ArtificialNeuralNetwork. OverviewandApplicationInRemoteSensingPresentedBy DeckyAspandiLatif 56070701073 Tuesday, November 12, 2013Computer Engineering King Mongkut University of Technology 2013 2. Introduction Remote Sensed Area Comprised of the Great amount of dataClassification useful for : Management, Monitoring, Administration,etcOther Classification Technique Manual DigitizationSupervised & unsupervised.Tuesday, November 12, 2013 3. Basic of ANN Researcher's Attempt to model the Human brain.Mimic the Structure and Learning processSupervised and Unsupervised LearningTuesday, November 12, 2013 4. Backpropagation ANN in Brief Learning from the data set (supervised) Using weight and activation function Error : 0.xx Feed forward generating output output 11 0 1i1i20.3 0.5 -0.2 -0.8 0.7 0.2 0.8 1.1 -1.4 -1.3Tuesday, November 12, 20130.2-0.9 -0.8 0.4 0.3-0.70.80.7 5.71.3 1.2 -0.2 -0.3 0.2E Propagate Feed Forward Feed Forward1.2 o1Propagate the error update weight learning ! Real Data Training Data No. i1 i2 o1 o2 10.1 o2111 0 ?1 ?20 100 1 ?0 1 ?3011 ?0 ?.... ?. ?.... ?. ?10001 001 0 ?1 0 ?Prediction Iteration 5. RS Classification ANN ANN take account on Learning the patternExperience classify automatically. No.Tuesday, November 12, 2013Water2Road .7output1.InputTypeGrass 6. Application (Basic Idea) Normalize (0-255) (0 1) No.B2B4o1o2o3o4No.Typeo1o2o3o410.70.200011Water000120.10.300102Road001030.60.20001.............7Grass011110000.50.40111Tuesday, November 12, 2013 7. Previous Research Remote sensing image classification based on artificial neural network: A case study of Honghe Wetlands National Nature Reserve, wang et all, 2010 Classification employed to monitor the Wetland environment 6 of 8 Bands of Thematic Mapper (TM) used as input paired with 7 output classes Purification is entangled to remove error in imagery boost classification accuracy. Comparison is employed to see the effectivenessTuesday, November 12, 2013 8. Application Cont.. The Classification ResultTuesday, November 12, 2013 9. Application Cont.. The improvement of the classification is about 8% 71% - 79%With > 70% accuracy, potential to be used in some cases.Tuesday, November 12, 2013 10. Conclusion ANN is Try to model how brain works. Learning is done through the updated weight along the iteration. ANN is applicable to RSS through imagery classification by learning the pattern of pixel band value. Potential of ANN is acceptable, and can greatly increased by some enhancementTuesday, November 12, 2013 11. TheEnd.ThankYou.Tuesday, November 12, 2013Computer Engineering King Mongkut University of Technology 2013