STRUCTURAL HIGH-RESOLUTION SATELLITE IMAGE INDEXING

By Gui-Song Xia, Wen Yang, Julie Delon, Yann Gousseau, Hong Sun, Henri Maître. 2010

343 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Liangpei Zhang
4. Xiaoqiang Lu
5. Guisong Xia
6. Xiang Bai
7. Gui-Song Xia
8. Yanfei Zhong

Citations in the corpus

• EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (2017, background): “create novel datasets, [22], [27], [28], [30] such as the two benchmark datasets PatternNet [37] and NWPU-RESISC45 [7].”
• A Comparative Study of Sampling Analysis in the Scene Classification of Optical High-Spatial Resolution Remote Sensing Imagery (2015, methodology): “, RS19 dataset [53], for evaluating, and each of them will be described in what follows.”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “, UC-Merced data set [58] and the WHURS19 data set [57]) to fully train the networks, such as CaffeNet [92] or GoogLeNet [84], showed a drop in accuracies compared with using the networks as global feature extractors.”
• Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery (2018, unknown): “, AlexNet and GoogLeNet) for classification of three well-known datasets, including UCMerced land-use [46], RS19 dataset [47], and Brazilian Coffee Scenes [48].”
• Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery (2016, methodology): “The WHU-RSdataset [4] is a new publicly-available dataset, which consists of 950 images with a size of 600 × 600 pixels uniformly distributed in 19 scene classes.”
• Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks (2016, methodology): “Existing methods for solving the object recognition task usually rely on segmentation and feature extraction [9–15].”
• Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification (2015, background): “Nevertheless, they bring many challenging problems in image understanding and information mining [1]–[3] as well.”
• Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery (2015, methodology): “The WHU-RS dataset [6], collected from Google Earth (Google Inc.”
• EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, methodology): “It is comprised of 45 scene categories, with each category containing 700 RS images.”
• Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge (2015, background): “− Beijing Dataset [17]: This dataset consists of a large high-resolution satellite image captured by GeoEye-1 of Majuqiao Town, located in the southwest Tongzhou District in Beijing.”
• Land-cover classification with high-resolution remote sensing images using transferable deep models (2018, background): “n has been taken into consideration for land-cover classication [13{17]. Spectral-spatial classication incorporates spatial information, such as texture [53{ 55], shape [56], and structure features [57,58], to improve the separability of dierent categories in the feature space. It has been reported ”
• Deep learning for remote sensing image classification: A survey (2018, methodology): “Nogueira et al. (2016) proposed a method that takes the UC-Merced data set (Xia et al., 2010) to fine-tune certain high-level layers of the GoogLeNet (Szegedy et al., 2014), achieving an impressive result.”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, methodology): “Starting with the UC-Merced data set [46], some representative data sets include WHU-RS19 [123], Brazilian Coffee Scene [77], Fig.”