Remote Sensing Image Classification: No Features, No Clustering

By Shiyong Cui, Gottfried Schwarz, Mihai Datcu. 2015

36 citations recorded by the source corpus.

In this paper, we consider the problem of remote sensing image classification, in which feature extraction and feature coding are critical steps. Various feature extraction methods aim at an abstract and discriminative image representation. Most of them are either theoretically too complex or practically infeasible to compute for large datasets. Motivated by this observation, we propose a simple yet efficient feature extraction method within the bag-of-words (BoW) framework. It has two main innovations. First and most interestingly, this method does not need any complex local feature extraction; instead, it uses directly the pixel values from a local window as low level features. Second, in contrast to many unsupervised feature learning methods, a random dictionary is applied to feature space quantization. The advantage of a random dictionary is that it does not need the time-consuming process of dictionary learning yet without a significant loss of classification accuracy. These two novel improvements over state-of-the-art methods significantly reduce the computational time and enable it scalable to a large data volume. An extensive experimental evaluation has been performed and compared with other feature extraction methods. It is demonstrated that our feature extraction method is quite competitive and can achieve rather promising performance figures for both optical and SAR satellite images.

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. Yanfei Zhong
2. Gui-Song Xia
3. Jingwen Hu
4. Liangpei Zhang
5. Fan Hu
6. Wei Yao
7. Otmar Loffeld
8. Mihai Datcu

In Media Technology

1. Yanfei Zhong
2. Gui-Song Xia
3. Liangpei Zhang
4. Jingwen Hu
5. Fan Hu
6. Wei Yao
7. Otmar Loffeld
8. Mihai Datcu

Citations in the corpus

• Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery (2016, result): “BOW [7] 71.86 SPM [30] 74 SCK [32] 72.52 SPCK++ [7] 77.38 SC + pooling [6] 81.67 ± 1.23 SG+ UFL [39] 82.72 ± 1.18 NFNC [71] 87.67 UFL-SC [40] 90.26 ± 1.51 COPD [12] 91.33 ± 1.11 ± 85.53 ± 1.24”
• Application and Evaluation of a Hierarchical Patch Clustering Method for Remote Sensing Images (2016, methodology): “Alternatively, the BoW features were constructed using the raw pixel values as the primitive feature descriptors, as suggested by [32].”