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MSAVI
Algorithm 3 Algorithm for clipping of Guwahati city raster Input 1: I: Corrected raster S and new raster Sn Input 2: Vs: Set of pixels within the Guwahati city administrative boundary. Output: Clipped raster with Guwahati city extent
Process:
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1) Initialized vector layer Sc = s0,s1,s2,s3. ...,sn−1, where
Sc ∈ set of pixels of the raster ranging from 1 to n 2) For every ith pixel in S: do 3) if Si ∈ Vs then: 4) Sn.append (Si) 5) i =i +1 6) else 7) Si → 0 8) i =i +1 9) End if 10) End for Save single raster (S)
V. RESULTS AND ANALYSIS
The CNN algorithm was used to create the LULC classifier on the Guwahati city dataset in Python. This dataset is used to classify 5 different classes. These five classes viz. built-up areas, water areas, vegetation, land, and others have been considered for classification. The actual map and the predicted classification map for the year 2011 of Guwahati cityare shown in Figure 3 and Figure 4 respectively. CNN is used to train the model. For CNN, experiments are carried out in the Python Tensorflow environment. It uses 0.0001 learning rate with 16 batch size. During m o d e l training the following parameters were used: Adam (decay = 1e-6, momentum = 0.9, Nesterov = True), tanh activation function is used in the feature extraction part i.e. convolution layer and ReLU activation functions in fully connected layer, and filter sizes of 5, 3, 1 with number of filters 16, 32 and 48 is used in convolution layer 1, 2 and 3 respectively. CNN is made up of three convolutional layers followed by a fully linked layer having 1000 nodes. The number of samples gathered for 5 classes for the Guwahati citydatasets is shown in table V. The training samples obtained are downsampled from a higher number of pixels to the lowest number of pixels. In this study for water has th e lowest pixels viz. 7945 as shown in table V. So, we downsampled all the class to the lowest number of samples (7945) during training. 80% percent of the samples were used for training, while 20% were used for testing. Table VI shows the effect of test accuracy on changing various parameters. It has been observed from Set 8 that the accuracy goes down if we use dropouts during training. The accuracy works well if we set the learning rate to 0.0001. The models are also tested on varying kernel sizes, it is seen that if we have kernel size of (5, 3, 1) for the first three convolution layer the test accuracy is high. In our experiment, we found that the parameters at Set 1 have given classification accuracy of 93.29% for our study area dataset. The kappa coefficient of the model is found to be 91.69%. The same set of parameters is also used to train and test model using basic spectral bands. The model generated with only 6 basic spectral bands is evaluated by metric like accuracy, precision, F1-score and recall as shown in Table IX. It is seen from the table VIII and IX that the model can classify the classes more precisely when the 10 indices are stacked with the 6 basic spectral bands of the multispectral Landsat image in comparison with only basic bands. The various metric used to evaluate the model are discussed below
