A Multilevel Stratified Spatial Sampling Approach for the Quality Assessment of Remote-Sensing-Derived Products

Huan Xie, Xiaohua Tong, Wen Meng, Dan Liang, Zhenhua Wang, Wen Zhong Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


With the advent of new remote sensors, the number and volume of remote-sensing data and its derived products, which are regarded as typical 'big data,' have grown exponentially. However, it remains a significant challenge to evaluate the quality of these big remote-sensing data and their derived products. Spatial sampling is necessary for the quality assessment of remote-sensing data and the derived products. This paper proposes an approach of multilevel stratified spatial sampling for the quality assessment of remote-sensing-derived products, with the aim of resolving the issue of the quality inspection of remote sensing big data and the derived products. The proposed multilevel stratified strategy: 1) makes full use of the prior knowledge of the data set; 2) selects a sample subset to get an unbiased estimator for the quality; 3) aims to acquire knowledge about the entire product; and 4) makes an evaluation based on statistical inference. The proposed method improves the sampling accuracy without increasing the inspection cost, and the whole procedure is repeatable and easily adopted for the quality inspection of remote-sensing-derived products and other geospatial data.
Original languageEnglish
Article number7128315
Pages (from-to)4699-4713
Number of pages15
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number10
Publication statusPublished - 1 Oct 2015


  • Multilevel stratified sampling
  • quality assessment
  • remote sensing big data
  • remote-sensing-derived products

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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