Remote Sensing Image Retrieval With Global Morphological Texture Descriptors

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Classifying Compound Structures in Satellite Images: A Compressed Representation for Fast Queries (2015, methodology): “For example, scale-invariant feature transform (SIFT)/Speeded Up Robust Features (SURF) [14], [19] are computed at each DoG key-point, or shape descriptors are derived for each MSER [20] and connected component [17], [21].”
Local structure learning in high resolution remote sensing image retrieval (2016, background): “So, compared to the statistical information of raw pixels, the information of these features is more robust [17] [18] [19].”
High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder (2015, background): “Specially, texture features, such as Gabor features, have been widely investigated due to its periodicity, coarseness and directionality characteristics (Aptoula 2014).”
PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval (2017, background): “Aptoula explored the potential of recently developed multiscale texture descriptors, the circular covariance histogram and the rotation-invariant point triplets, for the problem of geographic image retrieval, and introduced several new descriptors based on the Fourier power spectrum (Aptoula, 2014).”