Yanfei Zhong

TL;DR: The Aerial Image Data Set (AID) as mentioned in this paper is a large-scale data set for aerial scene classification, which contains more than 10,000 aerial images from remote sensing images.

TL;DR: The Aerial Image data set (AID), a large-scale data set for aerial scene classification, is described to advance the state of the arts in scene classification of remote sensing images and can be served as the baseline results on this benchmark.

TL;DR: Experimental results on UC Merced and Google data sets of SIRI-WHU demonstrate that the proposed method outperforms the state-of-the-art scene classification methods for HSR imagery.

TL;DR: An improved pre-trained AlexNet architecture named pre- trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling—spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations.

TL;DR: A Dirichlet-derived multiple topic model (DMTM) is proposed to fuse heterogeneous features at a topic level for HSR imagery scene classification and is able to reduce the dimension of the features representing the HSR images, to fuse the different types of features efficiently, and to improve the performance of the scene classification over that of other scene classification algorithms based on spatial pyramid matching, probabilistic latent semantic analysis, and latentDirichlet allocation.