Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery

By Yanfei Zhong, Qiqi Zhu, Liangpei Zhang. 2015

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

In Media Technology

1. Liangpei Zhang
2. Xiaoqiang Lu
3. Bo Du
4. Yanfei Zhong
5. Gui-Song Xia
6. Lefei Zhang
7. Gong Cheng
8. Jingwen Hu

Citations in the corpus

• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, unknown): “image classification [30], [66], [76], [78]–[84].”
• Large patch convolutional neural networks for the scene classification of high spatial resolution imagery (2016, background): “21 – 25 These approaches have shown competitive and promising performances in HSR-RS datasets.”
• Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery (2016, background): “Moreover, the visual words acquired by clustering the long feature vector using k-means clustering are not adequate [10].”
• Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network (2017, background): “Due to the noise and occlusions under cars and trees in the very high resolution (VHR) remote sensing images [11], [12], the existing methods often obtain heterogeneous results.”
• Land Use Classification in Remote Sensing Images by Convolutional Neural Networks (2015, background): “In [32], in particular, a hierarchical scheme for multiple feature fusion (HMFF) is proposed.”
• Scene Classification via a Gradient Boosting Random Convolutional Network Framework (2016, methodology): “To bridge this gap, some authors have applied latent Dirichlet allocation [6]–[8], a hierarchical probabilistic model, for the unsupervised learning of the topic allocation, based on the visual word distribution.”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “This data set is widely used for the task of aerial image classification [2]–[4], [7], [8], [10], [13], [15], [16], [18]–[20], [22]–[27], [30], [32]–[39], [58].”
• Classifying urban land use by integrating remote sensing and social media data (2017, methodology): “preserve a sufficient amount of spatial information (Zhong et al. 2015).”
• Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification (2017, background): “Traditional HSR remote sensing imagery understanding is based on recognizing pixel-based or object-based ground elements, but this cannot describe the whole content of the scene images and cannot well bridge the “semantic gap” between the low-level features and the high-level semantics [4].”
• SEN12MS - A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion (2019, background): “USGS SIRI-WHU 1 10,000×9,000 Color aerial image 4 classes over Montgomery County, Ohio, USA (Zhong et al., 2015)”