Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

By Fan Hu, Gui-Song Xia, Jingwen Hu, Yanfei Zhong, Kan Xu. 2016

26 citations recorded by the source corpus.

Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

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. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Chao Tao
5. Yansheng Li
6. Yongjun Zhang
7. Qiang Shen
8. Ying Li

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Chao Tao
5. Yongjun Zhang
6. Qiang Shen
7. Ying Li
8. Xizhe Xue

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, methodology): “Xiaoqiang Lu is with the Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, Shaanxi, P.R. China. analysis (OBIA) [71] or geographic object ba”
• Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion (2016, methodology): “Furthermore, the extraction process of the proposed unsupervised features can be accelerated by high-performance hardware or the integer quantization skill [44].”
• Deep learning for remote sensing image classification: A survey (2018, methodology): “Therefore, the deeper the network, the more strongly the learned features facilitate the natural image processing task, although such networks may lead to worse performance in classifying aerial scenes (Xia et al., 2016).”