Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning

By Xiwen Yao, Junwei Han, Gong Cheng, Xueming Qian, Lei Guo. 2016

325 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Liangpei Zhang
4. Guisong Xia
5. Xiaoqiang Lu
6. Xiang Bai
7. Xuelong Li
8. Peicheng Zhou

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Guisong Xia
5. Xiaoqiang Lu
6. Peicheng Zhou
7. Marco Körner
8. Lei Guo

Citations in the corpus

• Building Instance Classification Using Street View Images (2018, background): “…studied for decades, such as scale-invariant feature transform (SIFT) [21] encoded by bag of visual words (BoVW) [22, 23, 24], multiple textural features [25], 3D features derived from a digital surface model [26] and features learned by sparse coding methods [27, 28, 29, 30, 31, 32, 33, 13, 34].”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, background): “This type of methods also achieves the state-of-the-art performance on the aerial scene analysis [4], [33]–[35], [38], [41], [42], [88], [89].”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “[95] integrated pairwise constraints into a stacked sparse autoencoder to learn more discriminative features for land-use scene classification and semantic annotation tasks.”
• Scene classification of high resolution remote sensing images using convolutional neural networks (2016, background): “Scene classification of high resolution remote sensing images plays an important role for a wide range of applications, such as land-use determination, natural hazards detection, urban planning, geospatial object detection and so on [4-17].”
• Exploring Models and Data for Remote Sensing Image Caption Generation (2017, background): “W ITH the development of remote sensing technology, remote sensing images have become available and attracted extensive attention in numerous applications [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15].”
• Geography-Aware Self-Supervised Learning (2020, methodology): “In particular, we can improve the accuracy on target applications utilizing image recognition [5], object detection [39, 1], and semantic segmentation [46].”
• When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs (2018, methodology): “This kind of method automatically learns features from the raw input data using deep-architecture neural networks such as stacked autoencoder [37] and CNNs [7]–[12], [38]–[42].”
• A Survey of the Four Pillars for Small Object Detection: Multiscale Representation, Contextual Information, Super-Resolution, and Region Proposal (2022, background): “The small objects detection from aerial perspective [41], [42] is not the main content of our survey.”
• Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks (2017, methodology): “To acquire the semantic information of images, some studies have applied semantic models to extract semantic information from remote sensing images [13]–[15].”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, background): “increasingly available and bringing us the opportunity to try more research in military and civilian applications, such as natural disaster detection [3], [4], land-cover/land-use classification [5], [6] geographic space object detection [7], [8], geographic image retrieval [9], [10], urban planning”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, background): “that has been proposed for high resolution optical data [93], has not been explicitly explored for classification tasks using SAR data.”