Abstract
Our aim is to propose a new deep learning framework approach which uses an ensemble of convolutional neural network (CNN) for land use-land cover mapping. Every CNN layer was fed with diverse combination of multispectral and geospatial satellite bands provided by Sentinel 2 satellite imagery (spatial resolution of 10 m), topographic and derived texture parameters, of New Delhi (28.6139° N, 77.2090° E) region, India. Several classes were identified like forest, parking, residential areas, slums, wasteland, water bodies. It was observed that our proposed framework outperformed with classification accuracy of 89.43%, compared to the current state-of-the-art algorithms (support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF)). Accuracy assessment was done by means of following statistic measures (precision, recall, specificity, and area under curve (AUC)) and receiver operating characteristic (ROC) curve.
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Nijhawan, R., Joshi, D., Narang, N., Mittal, A., Mittal, A. (2019). A Futuristic Deep Learning Framework Approach for Land Use-Land Cover Classification Using Remote Sensing Imagery. In: Mandal, J., Bhattacharyya, D., Auluck, N. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 702. Springer, Singapore. https://doi.org/10.1007/978-981-13-0680-8_9
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