Using convolutional features and a sparse autoencoder for land-use scene classification

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Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, unknown): “In [91], Othman et.”
Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast (2020, methodology): “Othman et al. [30] proposed using a sparse autoencoder (AE) over pretrained features to generate the ﬁnal representation of RS scenes.”
Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, methodology): “proposed in [24], convolutional features and sparse autoencoder for VHR scene classification cited in [19], land-use classification in remote sensing images by CNN referenced in [23], the approach described in [20], and the two scenarios developed in [21].”
An object-based convolutional neural network (OCNN) for urban land use classification (2018, methodology): “Convolutional neural networks (CNNs), as a well-95 established and popular deep learning method, has produced state-of-the-art results for multiple 96 domains, such as visual recognition (Krizhevsky et al., 2012), image retrieval (Yang et al., 97 2015) and scene annotation (Othman et al., 2016).”
Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, background): “3) Deep Learning Features: As the active performance of deep learning, almost all the state-of-the-art approaches about remote sensing scene classification are based on CNN [15]–[17], [25], [26], [38]–[42].”