Abstract
The advent of neural networks has led to the development of image classification algorithms that are applied to different fields. In order to recover the vital spatial factor parameters, for example, land cover and land utilization, image grouping is most important in remote sensing. Recently, benchmark classification accuracy was achieved using convolutional neural networks (CNNs) for land cover classification. The most well-known tool which indicates the presence of green vegetation from multispectral pictures is the Normalized Difference Vegetation Index (NDVI). This chaper utilizes the success of the NDVI for effective classification of a new satellite dataset, SAT-4, where the classes involved are types of vegetation. As NDVI calculations require only two bands of information, it takes advantage of both RED- and NIR-band information to classify different land cover. The number and size of filters affect the number of parameters in convolutional networks. Restricting the aggregate number of trainable parameters reduces the complexity of the function and accordingly decreases overfitting. The ConvNet Architecture with two band information, along with a reduced number of filters, was trained, and high-level features obtained from a tested model managed to classify different land cover classes in the dataset. The proposed architecture, results in the total reduction of trainable parameters, while retaining high accuracy, when compared with existing architecture, which uses four bands.
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References
Dixon KDM, Ajay A, Sowmya V, Soman KP (2016) Aerial and satellite image denoising using least square weighted regularization method. Indian J Sci Technol 9(30)
Jeevalakshmi D, Narayana Reddy S, Manikiam B (2016) Land cover classification based on NDVI using LANDSAT8 time series: a case study Tirupati region. In: proceedings of IEEE international conference on communication and signal processing (ICCSP), pp 1332–1335
Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karki M, Nemani R (2015) Deepsat: a learning framework for satellite imagery. In: proceedings of 23rd SIGSPATIAL international conference on advances in geographic information systems, p 37
Papadomanolaki M, Vakalopoulou M, Zagoruyko S, Karantzalos K (2016) Benchmarking deep learning frameworks for the classification of very high resolution satellite multispectral data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3(7):83–88
Marmanis D, Wegner JD, Galliani S, Schindler K, Datcu M, Stilla U (2016) Semantic segmentation of aerial images with an ensemble of CNSS. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3:473–480
Makantasis K, Karantzalos K, Doulamis A, Doulamis N (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks. In: IEEE international geoscience and remote sensing symposium (IGARSS), pp 4959–4962
Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Kaiser P, Wegner JD, Lucchi A, Jaggi M, Hofmann T, Schindler K (2017) Learning aerial image segmentation from online maps. IEEE Trans Geosci Remote Sens 55(11):6054–6068
Sachin R, Sowmya V, Govind D, Soman KP (2017) Dependency of various color and intensity planes on CNN based image classification. In: International symposium on signal processing and intelligent recognition systems, pp 167–177
Vakalopoulou M, Karantzalos K, Komodakis N, Paragios N (2015) Building detection in very high resolution multispectral data with deep learning features. In: IEEE international geoscience and remote sensing symposium (IGARSS), pp 1873–1876
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
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Unnikrishnan, A., Sowmya, V., Soman, K.P. (2019). A Two-Band Convolutional Neural Network for Satellite Image Classification. In: Kumar, A., Mozar, S. (eds) ICCCE 2018. ICCCE 2018. Lecture Notes in Electrical Engineering, vol 500. Springer, Singapore. https://doi.org/10.1007/978-981-13-0212-1_17
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DOI: https://doi.org/10.1007/978-981-13-0212-1_17
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