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Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data
Volume 20, Issue 4 (2022): Special Issue: Large-Scale Spatial Data Science, pp. 545–565
Pub. online: 3 October 2022      Type: Statistical Data Science      Open accessOpen Access
Received
31 July 2022
Accepted
29 September 2022
Published
3 October 2022

Abstract

Global earth monitoring aims to identify and characterize land cover change like construction as it occurs. Remote sensing makes it possible to collect large amounts of data in near real-time over vast geographic areas and is becoming available in increasingly fine temporal and spatial resolution. Many methods have been developed for data from a single pixel, but monitoring pixel-wise spectral measurements over time neglects spatial relationships, which become more important as change manifests in a greater number of pixels in higher resolution imagery compared to moderate resolution. Building on our previous robust online Bayesian monitoring (roboBayes) algorithm, we propose monitoring multiresolution signals based on a wavelet decomposition to capture spatial change coherence on several scales to detect change sites. Monitoring only a subset of relevant signals reduces the computational burden. The decomposition relies on gapless data; we use 3 m Planet Fusion Monitoring data. Simulations demonstrate the superiority of the spatial signals in multiresolution roboBayes (MR roboBayes) for detecting subtle changes compared to pixel-wise roboBayes. We use MR roboBayes to detect construction changes in two regions with distinct land cover and seasonal characteristics: Jacksonville, FL (USA) and Dubai (UAE). It achieves site detection with less than two thirds of the monitoring processes required for pixel-wise roboBayes at the same resolution.

Supplementary material

 Supplementary Material
The construction of 2D Haar wavelets, a calculation for induced prior change probability, and sensitivity results are available in the Supplementary Material. The Jacksonville and Dubai dataset is proprietary, but code to generate and analyze a simulated dataset is included to demonstrate the algorithm.

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Copyright
2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
Bayesian change detection monitoring online remote sensing wavelet

Funding
This research is based upon work supported in part by the Office of the Director of National Intelligence (Intelligence Advanced Research Projects Activity) via 2021-20111000006. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. This material is based upon work supported in part by the National Science Foundation under Grant No. DMS-2152887.

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