Using texture to analyze and manage large collections of remote sensed image and video data.

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Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, methodology): “, are widely used for analyzing aerial or satellite images [51, 56, 62, 100-102].”
Modeling and Detection of Geospatial Objects Using Texture Motifs (2006, methodology): “Texture descriptors derived from Gabor filter banks have been widely used for browsing and similarity retrieval in image databases [27]–[29].”
High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder (2015, background): “Newsam et al. (2004) explored Gabor texture features to analyse and manage large collections of satellite imagery.”
Bag-of-visual-words and spatial extensions for land-use classification (2010, methodology): “These results are consistent with our previous use of homogeneous texture descriptors based on the outputs of Gabor filters for analyzing remote sensed imagery [21].”