Name
Title | ||
|---|---|---|
A Deep Learning Framework Approach For Urban Area Classification Using Remote Sensing Data |
Abstract | ||
|---|---|---|
The main aim of this study is to propose a Deep Learning framework approach for Urban area classification. The research proposes a multilevel Deep Learning architecture to detect the Urban/Non-Urban Area. The support models/parameters of the structure are Support Vector Machine (SVM), convolution of (Neural Networks) NN, high resolution sentinel 2 data, and several texture parameters. The experiments were conducted for the study region Lucknow which is a fast-growing metropolis of India, using Sentinel 2 satellite data of spatial resolution 10-m. The performance observed by the proposed ensembles of CNNs outperformed those of current state of art machine algorithms viz; SVM, Random Forest (RF) and Artificial Neural Network (ANN). It was observed that our Proposed Approach (PA) furnished the maximum classification accuracy of 96.24%, contrasted to SVM (65%), ANN (84%) and RF (88%). Several statistical parameters namely accuracy, specificity, sensitivity, precision and AUC, have been evaluated for examining performance during training and validation phase of the models. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-981-32-9088-4_37 | PROCEEDINGS OF 3RD INTERNATIONAL CONFERENCE ON COMPUTER VISION AND IMAGE PROCESSING, CVIP 2018, VOL 1 |
Keywords | DocType | Volume |
Deep learning, Convolution neural network, Remote sensing, Support vector machine, Urban area classification | Conference | 1022 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rahul Nijhawan | 1 | 0 | 0.34 |
| Radhika Jindal | 2 | 0 | 0.34 |
| Himanshu Dutt Sharma | 3 | 14 | 8.91 |
| Balasubramanian Raman | 4 | 679 | 70.23 |
| Josodhir Das | 5 | 0 | 0.34 |