Extended census transform histogram for land-use scene classification

Baohua Yuan; Shijin Li

doi:10.1117/1.JRS.11.025003

25 April 2017 Extended census transform histogram for land-use scene classification

Baohua Yuan, Shijin Li

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), Natural Science Foundation of China, Key R&D Program of Jiangsu Province, Ministry of Water Resources (MWR), Nonprofit Science and Technology Research Program of MWR, National High Resolution Earth Observation System, Water Resources Applications of National High Resolution Earth Observation System

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 025003 (April 2017). https://doi.org/10.1117/1.JRS.11.025003

Abstract

With the popular use of high-resolution satellite images, more and more research efforts have been focused on land-use scene classification. In scene classification, effective visual features can significantly boost the final performance. As a typical texture descriptor, the census transform histogram (CENTRIST) has emerged as a very powerful tool due to its effective representation ability. However, the most prominent limitation of CENTRIST is its small spatial support area, which may not necessarily be adept at capturing the key texture characteristics. We propose an extended CENTRIST (eCENTRIST), which is made up of three subschemes in a greater neighborhood scale. The proposed eCENTRIST not only inherits the advantages of CENTRIST but also encodes the more useful information of local structures. Meanwhile, multichannel eCENTRIST, which can capture the interactions from multichannel images, is developed to obtain higher categorization accuracy rates. Experimental results demonstrate that the proposed method can achieve competitive performance when compared to state-of-the-art methods.

Citation Download Citation

Baohua Yuan and Shijin Li "Extended census transform histogram for land-use scene classification," Journal of Applied Remote Sensing 11(2), 025003 (25 April 2017). https://doi.org/10.1117/1.JRS.11.025003

Received: 9 January 2017; Accepted: 3 April 2017; Published: 25 April 2017

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