Unsupervised Feature Learning for Aerial Scene Classification

By Anil M. Cheriyadat. 2014

413 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Representation & Self-Supervised Learning, Remote Sensing

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. Xiaoqiang Lu
5. Bo Du
6. Gui-Song Xia
7. Yanfei Zhong
8. Xuelong Li

In Media Technology

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

In Representation & Self-Supervised Learning

1. Liangpei Zhang
2. Gui-Song Xia
3. Yanfei Zhong
4. Fan Hu
5. Jingwen Hu
6. Bo Du
7. Gong Cheng
8. Junwei Han

In Remote Sensing

1. Liangpei Zhang
2. Gong Cheng
3. Junwei Han
4. Yanfei Zhong
5. Gui-Song Xia
6. Bo Du
7. Jingwen Hu
8. Fan Hu

Citations in the corpus

• Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery (2016, methodology): “In [6], a UFL method in which the sparse coding is used for learning sparse features is successfully applied to aerial scene classification.”
• Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022, methodology): “in an unsupervised way using models like sparse coding [16],”
• Land-use scene classification using multi-scale completed local binary patterns (2016, background): “In [12], three different feature extraction strategies including raw pixel intensity values, oriented filter responses and dense SIFTdescriptorswere investigated in the land-use scene classification framework.”
• Unsupervised Deep Feature Extraction for Remote Sensing Image Classification (2015, methodology): “learning of sparse features for aerial image classification [38].”
• Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors (2016, neutral): “0 Unsupervised feature learning [33] 81.”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, methodology): “However, most previous works tend to generate a global representation of the image with the same contribution of each part [13], [15]–[18], in spite of the negative effects of redundant areas.”
• Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery (2015, methodology): “Cheriyadat [4] used the sparse coding algorithm to learn sparse local features for image scenes and pool local features to generate image representations.”
• High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder (2015, methodology): “…Du, and Zhang (2015), the sparse auto-encoder network, an unsupervised feature learning method, was trained with both salient and non-salient patches to learn feature representations for aerial scene classification and achieved better performance than the approach introduced in Cheriyadat (2014).”
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, methodology): “Following the experimental setup in [58], we randomly selected 80% of the samples from each class for training and set the remaining images for testing.”
• Unsupervised Quaternion Feature Learning for Remote Sensing Image Classification (2016, methodology): “I. INTRODUCTION H AND-CRAFTED local feature representations, suchas scale-invariant feature transform (SIFT) and histograms of oriented gradients (HOG), dominate in the field of remote sensing image classification.”
• Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery (2015, methodology): “For instance, in [14], Cheriyadat explored an unsupervised feature learning approach to directly model an aerial scene by exploiting the local spatial and structural patterns for scene classification.”
• Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel Coding (2016, methodology): “Besides, [10] proposed an unsupervised feature learning method, in which the new sparse representations of the feature descriptors are generated by the low-level feature descriptors.”
• PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval (2017, methodology): “In (Cheriyadat, 2014), an unsupervised feature learning approach combining SIFT and sparse coding is proposed to learn sparse feature representations for aerial scene classification.”