Abstract
It is very important to extract key image features from high resolution in Remote Sensing imagery in order to serve various purposes either from the side of government or from the side of economical and ethical issues. With the advent of big data Technologies high resolution imagery can be easily managed, but at the same time with the curse of dimensionality, there exists a dilemma of which feature has to be selected. Extraction of important features and key features plays a prominent role in the whole methodology. Employing convolutional neural networks to handle these features is more crucial in these scenarios. Extracting of features from these sorts of images will be very fruitful for various applications and this can be achieved by employing feature selection in the classification of High-Resolution Remote Sensing images, which involves the use of Apache Spark, second-order Grey Level Co-occurrence Matrix features for additional processing and Convolutional Neural Networks.
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Giridhar Sai, K., Sujatha, B., Tamilkodi, R., Leelavathy, N. (2023). High Resolution Remote Sensing Image Classification Using Convolutional Neural Networks. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_24
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