Land Use Classification Using Multi-neighborhood LBPs
This work addresses the challenge of classifying land use images with intra-class variability and inter-class similarities, but it is incremental as it builds on existing LBP methods.
The paper tackled land use image classification by proposing multi-neighborhood local binary patterns (LBPs) combined with a nearest neighbor classifier, achieving an accuracy of 77.76% on the UC Merced dataset.
In this paper we propose the use of multiple local binary patterns(LBPs) to effectively classify land use images. We use the UC Merced 21 class land use image dataset. Task is challenging for classification as the dataset contains intra class variability and inter class similarities. Our proposed method of using multi-neighborhood LBPs combined with nearest neighbor classifier is able to achieve an accuracy of 77.76%. Further class wise analysis is conducted and suitable suggestion are made for further improvements to classification accuracy.