Land-Use Classification With Compressive Sensing Multifeature Fusion

By Mohamed Lamine Mekhalfi, Farid Melgani, Yakoub Bazi, Naif Alajlan. 2015

75 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, 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 Computational Mechanics

1. Hongxun Yao
2. Yanfeng Gu
3. Souleyman Chaib
4. Gong Cheng
5. Junwei Han
6. Jocelyn Chanussot
7. Naif Alajlan
8. Farid Melgani

In Computer Vision and Pattern Recognition

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Xiaoqiang Lu
5. Jocelyn Chanussot
6. Guisong Xia
7. Lei Guo
8. Xiwen Yao

In Electrical and Electronic Engineering

1. Hongxun Yao
2. Yanfeng Gu
3. Souleyman Chaib
4. Gong Cheng
5. Junwei Han
6. Jocelyn Chanussot
7. Xuelong Li
8. Liangpei Zhang

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Xiaoqiang Lu
5. Jocelyn Chanussot
6. Lei Guo
7. Guisong Xia
8. Yanfei Zhong

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “dataset is the most popular and has been widely used for the task of remote sensing image scene classification and retrieval [8, 10, 11, 14-16, 18-20, 24, 26, 28-30, 36, 38, 53, 55, 59, 61, 62, 81, 82, 84-98].”
• Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery (2017, background): “6% overall classification accuracy using completed LBP, [12] at 94.”
• An Informative Feature Selection Method Based on Sparse PCA for VHR Scene Classification (2016, methodology): “We used sPCA to select the useful features that are latterly combined with the linear SVM for scene classification; we then compared the scene classification performance of the proposed method with different approaches such as the spatial extension of BoVW (SPCK++) reported in [18], the SIFT+sparse ”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, methodology): “2) Unsupervised Learning Features: In the domain of remote sensing, many unsupervised methods have been used and have achieved better performance than those based on handcrafted features [13], [33]–[36].”
• Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, methodology): “The third data set named WHU-RS was exported from Google Earth [45].”
• Using convolutional features and a sparse autoencoder for land-use scene classification (2016, methodology): “In this approach termed CNN-AE, we view the classi fi cation problem from a reconstruction point of view in a similar way to the compressive sensing approach proposed in Mekhal fi et al. (2015).”
• When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs (2018, methodology): “Typical unsupervised feature learning methods include, but not limited to, principal component analysis, k-means clustering, sparse coding [26]–[28], [33], [44] and autoencoder [29], [31].”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, methodology): “The dropout method [59] operates on the fully connected layers to avoid overfitting because a fully connected layer usually”
• Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery (2016, methodology): “method [17], and it exceeds the state-ofthe-art performance with the UC Merced data set.”