A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

By Bei Zhao, Yanfei Zhong, L. Zhang. 2016

134 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology, 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 Atmospheric Science

1. Gong Cheng
2. Junwei Han
3. Liangpei Zhang
4. Lei Guo
5. Liqin Cao
6. Xiaobing Han
7. Yanfei Zhong
8. Xiaoqiang Lu

In Computer Vision and Pattern Recognition

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Xiwen Yao
5. Liangpei Zhang
6. Lei Guo
7. Yanfei Zhong
8. Hongxun Yao

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Liangpei Zhang
5. Xiwen Yao
6. Lei Guo
7. Yanfei Zhong
8. Yanfeng Gu

In Remote Sensing

1. Gong Cheng
2. Junwei Han
3. Yanfei Zhong
4. Liangpei Zhang
5. Lei Guo
6. Hongxun Yao
7. Yanfeng Gu
8. Huan Liu

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “Accordingly, these classes contain a variety of spatial patterns, some homogeneous with respect to texture, some homogeneous with respect to color, others not homogeneous at all.”
• Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification (2017, methodology): “From Figure 19, it can be seen that the pre-trained AlexNet-SPP-SS model performs better over the training sample ratios of [10, 20, 30, 40, 50] than the pre-trained AlexNet, the pre-trained AlexNet-SPP, and the pre-trained AlexNet-SS models for the Google image dataset of SIRI-WHU.”
• An object-based convolutional neural network (OCNN) for urban land use classification (2018, background): “The pixel-level approaches that rely purely upon spectral characteristics are 62 able to classify land cover, but are insufficient to distinguish land uses that are typically 63 composed of multiple land covers, and such problems are particularly significant in urban 64 settings (Zhao et al., 2016).”
• Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, background): “Recently, the research in [28] introduced the combination between the spectral and structural information of VHR images.”
• When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs (2018, methodology): “In addition, it is worth noting that the widely used codebook in bag of visual words-based methods [45]–[50] is often generated by performing unsupervised k-means clustering on the set of handcrafted feature descriptors or raw pixel intensity values.”
• Joint Deep Learning for land cover and land use classification (2019, background): “Commensurate with the rapid development in sensor technologies, a huge amount of very fine spatial resolution (VFSR) remotely sensed imagery is now commercially available, opening new opportunities for LULC information extraction at a very detailed level (Pesaresi et al., 2013; Zhao et al., 2016).”