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ABSTRACT
This paper introduces a new modified local pattern descriptor to extract road from rural areas’ aerial imagery. The introduced local pattern descriptor is actually the modification of the proposed local vector pattern (P-LVP), and it is named as Modified-PLVP (M-PLVP). In fact, M-PLVP extracts the texture features from both road and non-road pixels. The features are subjected to train the Deep belief Network (DBN); thereby the unknown aerial imagery is classified into road and non-road pixel. Further, to improve the classification rate of DBN, morphological operations and grey thresholding operations are performed and so that the road segmentation is performed. Apart from this improvement, this paper incorporates the optimisation concept in the DBN classifier, where the activation function and the count of hidden neurons are optimally selected by a new Trail-based WOA (T-WOA) algorithm, which is the improvement of the Whale Optimisation Algorithm (WOA). Finally, the performance of proposed M-PLVP is compared over other local pattern descriptors concerning measures like Accuracy, Sensitivity, Specificity, Precision, Negative Predictive Value (NPV), F1Score and Mathews correlation coefficient (MCC), False positive rate (FPR), False negative rate (FNR), and False Discovery Rate (FDR), and proves the betterments of M-PLVP over others.
Nomenclature
| P-LVP | = | proposed local vector pattern |
| M-PLVP | = | Modified-PLVP |
| DBN | = | Deep Belief Network |
| T-WOA | = | Trail-based WOA |
| WOA | = | Whale Optimization Algorithm |
| LBP | = | Local Binary Pattern |
| FDR | = | False Discovery Rate |
| LVP | = | Local Vector Pattern |
| CLBP | = | Complete Local Binary Pattern |
| CD | = | contrastive divergence |
| FPR | = | False positive rate |
| MCC | = | Mathews correlation coefficient |
| LTrP | = | Local Tetra Pattern |
| FCN | = | fully convolutional network |
| NPV | = | Negative Predictive Value |
| RSRCNN | = | road structure refined convolutional neural network |
| FNR | = | False negative rate |
| NN | = | neural network |
| DP | = | dynamic programming |
| FCM | = | Fuzzy C-means |
| RBM | = | Restricted Boltzmann Machine |
| CNN | = | Convolutional Neural Network |
| PSO | = | Particle Swarm Optimization |
| GWO | = | Grey Wolf Optimization |
Acknowledgments
I would like to express my very great appreciation to Dr Archana Bhise for his valuable and constructive suggestions during the planning and development of this research work. His willingness to give his time so generously has been very much appreciated. Also, I wish to thank my parents for their support and encouragement throughout my study.