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Kole, Alok
- Man-Made Object Extraction from Remote Sensing Images Using Gabor Energy Features and Probabilistic Neural Networks
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Aliah University, IN
2 Department of Electrical Engineering, Indian Institute of Technology, Dhanbad, IN
3 Department of Electrical Engineering, RCC Institute of Information Technology, IN
1 Department of Electronics and Communication Engineering, Aliah University, IN
2 Department of Electrical Engineering, Indian Institute of Technology, Dhanbad, IN
3 Department of Electrical Engineering, RCC Institute of Information Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2849-2859Abstract
This paper presents a novel approach for man-made object extraction in remote sensing images. This paper focuses on the design and implementation of a system that allows a user to extract multiple objects such as buildings or roads from an input image without much user intervention. The framework includes five main stages: 1) Pre-processing Stage. 2) Extraction of Local energy features using edge information and Gabor filter followed by down sampling to reduce the redundant information. 3) Further reduction of the size of feature vectors using Wavelet decomposition. 4) Classification and recognition of man-made structures using Probabilistic Neural Network (PNN) 5) NDVI based post-classification refinement. Experiments are conducted on a dataset of 200 RS images. The proposed framework yields overall accuracy of 93%. Experimental results validate the effective performance of the suggested technique for extracting man-made objects from RS images. Compared with other methods; the proposed framework exhibits significantly improved accuracy results and computationally much more efficient. Most notably, it has a much smaller input size, which makes it more feasible in practical applications.Keywords
Remote Sensing Image, Man-Made Object Extraction, Gabor Wavelets, Probabilistic Neural Network.References
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- A Deep Learning Approach for Road Extraction from Remote Sensing Imagery
Abstract Views :100 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Aliah University, IN
2 Department of Electrical Engineering, Indian Institute of Technology, Dhanbad, IN
3 Department of Electrical Engineering, RCC Institute of Information Technology, IN
1 Department of Electronics and Communication Engineering, Aliah University, IN
2 Department of Electrical Engineering, Indian Institute of Technology, Dhanbad, IN
3 Department of Electrical Engineering, RCC Institute of Information Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 2 (2023), Pagination: 2879-2889Abstract
In recent years, Deep Learning (DL) is proving very successful set of tools for several image analysis, segmentation, and classification tasks. In this paper an automated Deep Learning Architecture (DLA) called the Deep Belief Neural Networks (DBN) stacked by Restricted Boltzmann Machines (RBMs), is designed, implemented, and experimentally evaluated for extracting semantic maps of roads in Remote Sensing (RS) images. Representative features are extracted by unsupervised pre-training of DBN and supervised fine-tuning phase. A Logistic Regression (LR) is added to the end of feature learning system to constitute a DBN-LR architecture. This LR classifier is employed to fine-tune the whole pre-trained network in a supervised way and classifies the patches from RS images. The features extracted from the image patches are fed to the architecture as input and it produces the class labels as a probability matrix as either a positive sample (road) or a negative sample (non-road). A math morphology algorithm is used to improve DBN performance during post processing. Experiments are conducted on a dataset of 970 RS scene images of urban and suburban areas to demonstrate the performance of the proposed network architecture. The proposed deep model resulted in an Overall Accuracy (OA) of 96.57% and F1-score of 0.9552. The results of the proposed architectures are compared with those of other network architectures. Experimental results demonstrate the effective performance of the proposed method for extracting roads from a complex scene.Keywords
Remote Sensing Imagery, Road Networks Extraction, Deep Learning, Deep Belief Network, Restricted Boltzmann MachineReferences
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