Weighted multiscale region-level sparse representation for classification of high-spatial resolution remote sensing images

Sensen Chu; Liang Hong; Liang Cheng; Peijun Du; Wenwen Zong

doi:10.1117/1.JRS.12.045005

13 October 2018 Weighted multiscale region-level sparse representation for classification of high-spatial resolution remote sensing images

Sensen Chu, Liang Hong, Liang Cheng, Peijun Du, Wenwen Zong

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 4, 045005 (October 2018). https://doi.org/10.1117/1.JRS.12.045005

Abstract

Region segmentation is a basic and important procedure in region-based classification of remote sensing images. Compared with single-scale segmentation, multiscale segmentation can obtain different structure information and shows good performance in classification. Nonetheless, in the previous multiscale algorithms, different scales are regarded as having equal contributions to classification, thus, multiscale segmentation can contain some unsuitable scales, which affects the classification accuracy. To overcome this drawback and sufficiently utilize structural information, a weighted multiscale region-level sparse representation classification (WMRSRC) algorithm is proposed. In the WMRSRC algorithm, different weights are utilized for the region-level features of different scales. The weights are determined by the segmentation quality, which is evaluated by interregion and intraregion heterogeneity measures (Local Moran’s I and variance, respectively). Once the region weights on different scales are determined, the weighted joint sparse representation model is used to classify the multiscale regions. Through three classification experiments based on high-spatial resolution remote sensing images, we find that the proposed WMRSRC algorithm gives better results than other state-of-the-art algorithms.

Citation Download Citation

Sensen Chu, Liang Hong, Liang Cheng, Peijun Du, and Wenwen Zong "Weighted multiscale region-level sparse representation for classification of high-spatial resolution remote sensing images," Journal of Applied Remote Sensing 12(4), 045005 (13 October 2018). https://doi.org/10.1117/1.JRS.12.045005

Received: 23 January 2018; Accepted: 28 September 2018; Published: 13 October 2018

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