Abstract
Very high resolution (VHR) satellite imagery and image processing algorithms allow for the development of remote sensing applications including multi-temporal classification, tracking of specific targets, multimedia data integration, ecosystem processes analysis, and land cover/land use (LULC) mapping. Classification algorithms are the primary source to generate LCLU maps. Since texture information is essential to generate LULC maps from VHR images, the object-based classification methods should be used instead of pixel-based methods. Also, in urban mapping, it is vital to select the appropriate classifier according to the type of land covers. Recently, in addition to machine learning algorithms, deep learning methods have also been used to classify VHR images. In this study, we compare the accuracy of convolutional neural network (CNN) algorithm with some machine learning methods, for classification of Pleiades satellite image with 50 cm spatial resolution. The results showed CNN algorithm has the highest classification accuracy when the training samples are increased. However, the difference between the classification accuracy of the CNN and relevance vector machine (RVM) models is not that significant so that one could use a more straightforward method with less training data rather than a complicated one with large volumes of data.
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This work was supported by Shahid Rajaee Teacher Training University under Contract Number 19059.
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Mohammadi, M., Sharifi, A. Evaluation of Convolutional Neural Networks for Urban Mapping Using Satellite Images. J Indian Soc Remote Sens 49, 2125–2131 (2021). https://doi.org/10.1007/s12524-021-01382-x
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DOI: https://doi.org/10.1007/s12524-021-01382-x