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

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.

The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

“…Their results showed that object detection performance improves when using SR as a pre-processing step versus the native coarser imagery. Xu et al [42] use sparse dictionary learning to generate synthetic 8× and 16× superresolved imagery from Landsat and MODIS image pairs. Their results show an increase performance for land-cover change mapping when using the super-resolved imagery.…”

Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016

“…However, this requires abundant Landsat images (>4) (Xu et al, 2018a) that are not available in the humid tropical regions and may cause "false changes" and "inter-annual inconsistency" (Broich et al, 2011). Recently, a superresolution mapping method (Li et al, 2017;Qin et al, 2017;Xu et al, 2017) was used to reconstruct missing forest cover change 80 during 2011-2014 (Zhang et al, 2019) by fusing the PALSAR/PALSAR-2 and the MODIS normalized difference vegetation index (NDVI) with dense temporal resolution and phenological information. However, it is difficult to separate oil palm and natural forest with similar NDVI variation using such classification-based fusion.…”

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

“…The authors compared their approach with STARFM and observed that the performance was better. A paper by Xu et al [151] discusses a change detection approach that uses synthetically-generated images. A sparsity-based approach is used to learn the mapping between a pair of fine and coarse images.…”