ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 723–730, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-723-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 723–730, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-723-2020

  03 Aug 2020

03 Aug 2020

NOVEL CLASSIFICATION UNCERTAINTY MEASUREMENT MODEL INTEGRATING SPATIAL INFORMATION FOR REMOTE SENSING IMAGE

Q. Zhang, P. Zhang, and X. Hu Q. Zhang et al.
  • School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

Keywords: Remote sensing image, Image classification, Classification uncertainty, Uncertainty measurement model, Spatial information

Abstract. Remote sensing image classification has important applications in many fields. However, the uncertainty of remote sensing image classification results will reduce its application value and reliability in these applications. Therefore, the uncertainty of remote sensing image classification results must be accurately and effectively measured. To address the shortcomings of the existing classification uncertainty measurement model in the utilization of image spatial information, this study proposes a novel uncertainty measurement model for remote sensing image classification, which considers the spatial correlation between pixels in images and the effects of local spatial heterogeneity during uncertainty measurement. Specifically, the proposed model first measures the classification uncertainty of an image at the pixel and local spatial levels on the basis of the posterior probability of image classification. Second, the local spatial heterogeneity of an image is quantified, and the proposed model uses the local spatial heterogeneity of the image as a weight to adaptively fuse the uncertainties of the pixel and local spatial levels. Accordingly, a joint uncertainty measurement index is generated for a more accurate and effective evaluation of the uncertainty of remote sensing image classification. Lastly, the classification verification experiments on three publicly available remote sensing images with different spatial resolutions confirm the validity of the proposed model. Moreover, experimental results show that the proposed model has relative superiority and better stability than the existing and commonly used uncertainty measurement models (e.g., information entropy and Eastman’s U) in improving image classification performance.