Land-use Classification for High-resolution Remote Sensing Image using Collaborative Representation with a Locally Adaptive Dictionary

Mingxue Zheng, Huayi Wu

Abstract

Sparse representation is widely applied in the field of remote sensing image classification, but sparsity-based methods are time-consuming. Unlike sparse representation, collaborative representation could improve the efficiency, accuracy, and precision of image classification algorithms. Thus, we propose a high-resolution remote sensing image classification method using collaborative representation with a locally adaptive dictionary. The proposed method includes two steps. First, we use a similarity measurement technique to separately pick out the most similar images for each test image from the total training image samples. In this step, a one-step sub-dictionary is constructed for every test image. Second, we extract the most frequent elements from all one-step sub-dictionaries of a given class. In the step, a unique two-step sub-dictionary, that is, a locally adaptive dictionary is acquired for every class. The test image samples are individually represented over the locally adaptive dictionaries of all classes. Extensive experiments (OA (%) =83.33, Kappa (%) =81.35) show that our proposed method yields competitive classification results with greater efficiency than other compared methods.

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Paper Citation


in Harvard Style

Zheng M. and Wu H. (2018). Land-use Classification for High-resolution Remote Sensing Image using Collaborative Representation with a Locally Adaptive Dictionary.In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-294-3, pages 88-95. DOI: 10.5220/0006705300880095


in Bibtex Style

@conference{gistam18,
author={Mingxue Zheng and Huayi Wu},
title={Land-use Classification for High-resolution Remote Sensing Image using Collaborative Representation with a Locally Adaptive Dictionary},
booktitle={Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2018},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006705300880095},
isbn={978-989-758-294-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Land-use Classification for High-resolution Remote Sensing Image using Collaborative Representation with a Locally Adaptive Dictionary
SN - 978-989-758-294-3
AU - Zheng M.
AU - Wu H.
PY - 2018
SP - 88
EP - 95
DO - 10.5220/0006705300880095