Scene Classification via a Gradient Boosting Random Convolutional Network Framework

By Fan Zhang, Bo Du, Liangpei Zhang. 2016

398 citations recorded by the source corpus.

Subfields: Atmospheric Science, 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 Atmospheric Science

1. Liangpei Zhang
2. Bo Du
3. Qian Du
4. Lefei Zhang
5. Gong Cheng
6. Junwei Han
7. Yanfei Zhong
8. Wei Li

In Computer Vision and Pattern Recognition

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

In Media Technology

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

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “models have shown impressive feature representation capability for a wide range of applications including remote sensing image scene classification [13, 17, 45, 50, 82, 143-158].”
• Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, methodology): “In [17], a deep ensemble framework based on CNN was proposed for scene classification.”
• Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017, background): “Two good overviews of DL are the survey paper of Schmidhuber et al.56 and the book by Goodfellow et al.23 Zhang et al.3 give a general framework for DL in RS, which covers four RS perspectives: (1) image processing, (2) pixel-based classification, (3) target recognition, and (4) scene understanding.”
• Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data (2017, background): “Over past years, more and more studies have been using DL for processing of RS imagery [25], [26].”
• Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection (2016, background): “ing of hierarchical internal feature representations from image data sets [15]–[17].”
• AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “In [37], a Gradient Boosting Random Convolutional Network (GBRCN) was proposed for classifying aerial images with only two convolutional layers.”
• Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors (2016, background): “10 gradie t boosting randomconv lutional network [39] 94.”
• Deep learning for remote sensing image classification: A survey (2018, methodology): “In addition, HSI contains a variety of data and it is challenging to design robust, handcrafted features for HSI data (Zhang, Zhang, & Du, 2016).”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “different strategies of exploiting CNNs, a variety of CNN-based scene classification methods [73], [102]–[107] have emerged.”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, neutral): “76 Gradient Boosting Random CNNs [50] 94.”
• Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks (2016, background): “Ishii et al. [29] compared CNN with support vector machine (SVM) for surface object recognition from Landsat 8 imagery and found CNN to outperform FSM. Zhang and Zhang [30] presented a boosting random convolutional network (GBRCN) framework for scene classification.”
• Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification (2017, methodology): “[33,35] proposed an improved gradient boosting CNN ensemble framework to reuse the weights in each random convolutional network.”