individual tree counting using high resolution remotely sensed images

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4. MATERIAL AND METHODS4.4 ISODATA Unsupervised Classification.X2PixelClusterbD 2 E = (X i –M j ) 2ClusteraX1

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4. MATERIAL AND METHODSWINDOW: 5X5 PIXELSNoise Model: Multiplicative andAdditiveAdditive Noise Mean: 10Multiplicative Noise Mean: 10ORIGINAL IMAGEWINDOW: 5X5 PIXELSNoise Model: Multiplicative andAdditiveAdditive Noise Mean: 20Multiplicative Noise Mean: 20

4. MATERIAL AND METHODS4.4 ISODATA Unsupervised Classification.X2PixelClusterbD 2 E = (X i –M j ) 2ClusteraX1

Page 2 and 3: PRESENTATION OVERVIEW• Introducti
Page 4 and 5: 2. OBJECTIVESOur main goal was to c
Page 6 and 7: 3. BACKGROUND3.2 Why counting trees
Page 8 and 9: 4. MATERIAL AND METHODS4.2 Methodol
Page 12 and 13: 4. MATERIAL AND METHODS
Page 14 and 15: 5. RESULTS5.1 Class that represente
Page 16 and 17: 5. RESULTS5.3 Comissiom Errors
Page 18 and 19: 6. CONCLUSIONSA methodological appr
Page 20: THANK YOU !Contacts:fredreis1@gmail

individual tree counting using high resolution remotely sensed images

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