Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Abstract

Abstract:Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method-a measure for the spectral distortion of change information-is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

Targeted change detection in remote sensing images

“…Further researches described in [30,37,45,32,31] give promising results, but most of them share the same drawback: low amount of data used for the method validation. The of creation a large and reliable dataset is one of the keys to the development of robust and reliable algorithms for the targeted change detection.…”

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

“…The change detection algorithm used is the iteratively reweighted multivariate alteration detection (IR-MAD) method. According to [92], the proposed method yielded good change detection results in water and vegetation areas. One issue with this paper is that only one method (IR-MAD) was used, while other state-of-the-art methods, and perhaps better and newer algorithms, were not.…”

“…The idea presented in a paper by Wang et al [92] is interesting. The key innovation is to generate four cross pansharpened images for a given pair of MS images with pan band.…”

“…A paper by Park et al [97] presents an interesting and unsupervised approach to change detection. The key idea is the use of cross-sharpened images, as used in another paper [92], to reduce false alarms due to relief displacement and seasonal changes. A cross-sharpened image is generated when the pan band is collected at a different time from that of the MS bands.…”

Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

“…Byun et al [19] applied cross-sharpening techniques using near-infrared (NIR) bands to effectively detect the changed area in a flood region. Wang et al [20] confirmed that the change detection accuracy can be increased by applying the IR-MAD technique to cross-sharpened images.…”