Land-use scene classification: a comparative study on bag of visual word framework

Abstract

With successful launch of high spatial resolution (HSR) sensors, highly detailed spatial information is provided for remote sensing research. This improvement has allowed researchers to monitor environmental changes on a small spatial scale. However traditional pixel-based classification approaches are not able to interpret high spatial resolution remote sensing imagery effectively. Bag of visual words (BoVW) framework, on the other hand, is becoming one of the most popular approaches to validate the performance of remote sensing image datasets. While pixel-based approaches may not fully describe very high-resolution remote sensing images, BoVW model is narrowing the gap between low-level features and high-level semantic features by generating an intermediate description of image features. This paper presents a comparative study to evaluate the potential of using different coding approaches of BoVW model to solve the land-use scene classification problem. Initially, this work summarizes different configurations of BoVW framework in coding and clustering. Later, we perform an extensive evaluation of BoVW on land-use scene classification and retrieval. Finally we draw several conclusions regarding different coding strategies of BoVW, codebook size and number of training images. The approach is validated on two commonly used datasets in remote sensing, UC Merced a 21-class land-use dataset and RSDataset a 19-class satellite scene dataset.