High-Resolution Remote-Sensing Image Classification via an Approximate Earth Mover's Distance-Based Bag-of-Features Model

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Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, methodology): “they are generally used as building blocks to construct global image features, such as the well-known bag-of-visual-words (BoVW) models [6, 8, 9, 14, 19, 29, 36, 38, 39, 55, 93, 101, 122, 123] and HOG feature-based part models [22, 23, 27, 103].”
Land Use Classification in Remote Sensing Images by Convolutional Neural Networks (2015, background): “In [20], instead, histograms of dense SIFT features are matched through a fast approximation of the Earth movers distance.”
Land-Use Scene Classification Using a Concentric Circle-Structured Multiscale Bag-of-Visual-Words Model (2014, methodology): “Although these variants of the original BOVW model have a good descriptive ability to depict spatial layout information of scene images, they are designed mainly for natural image scene classification, and seldom have they been applied in the field of remote sensing image scene classification, excep”