Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery

Yong Xu,Deyu Meng,Lin Lin

doi:10.3390/rs9070709

Abstract

Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in difficulty in detecting subtle spatial variations within a coarse pixel—especially for a fast-growing city. Given that the historical land use/cover products and satellite data at finer resolution are valuable to reflect the urban dynamics with more spatial details, finer spatial resolution images, as well as land cover products at previous times, are exploited in this study to improve the change detection capability of coarse resolution satellite data. The proposed approach involves two main steps. First, pairs of coarse and finer resolution satellite data at previous times are learned and then applied to generate synthetic satellite data with finer spatial resolution from coarse resolution satellite data. Second, a land cover map was produced at a finer spatial resolution and adjusted with the obtained synthetic satellite data and prior land cover maps. The approach was tested for generating finer resolution synthetic Landsat images using MODIS data from the Guangzhou study area. The finer resolution Landsat-like data were then applied to detect land cover changes with more spatial details. Test results show that the change detection accuracy using the proposed approach with the synthetic Landsat data is much better than the results using the original MODIS data or conventional spatial and temporal fusion-based approaches. The proposed approach is beneficial for detecting subtle urban land cover changes with more spatial details when multitemporal coarse satellite data are available.

Highlights

And accurate information about land cover dynamics is highly important for sustainable urban development and better quality of life in cities
When the two downscaling approaches are compared with each other, it is found that the learning-based approach performs slightly better than the conventional STARFM method in terms of all tested indices
Further experiments demonstrate that it is better than the conventional downscaling approach STARFM when both predicted synthetic data are applied for land cover changes (LCCs) detection

Summary

Introduction

And accurate information about land cover dynamics is highly important for sustainable urban development and better quality of life in cities. Compared with conventional data collection methods like field surveying and aerial photography, satellite images have proven to be more effective and efficient for land use/cover change monitoring at regional or global scales due to their timely, consistent, repeatable, and cost-effective measurements [1,2]. A wide variety of change detection approaches have been formulated, ranging from preclassification methods such as image differencing, image ratioing [3], band analysis [4], principal component analysis [5], change vector analysis [6], and composite analysis to postclassification comparisons [7]

Methods

Findings

Discussion

Conclusion