Abstract
The application of remote sensing techniques for disaster management often requires rapid damage assessment to support decision-making for post-treatment activities. As the on-demand acquisition of pre-event very high-resolution (VHR) images is typically limited, PlanetScope (PS) offers daily images of global coverage, thereby providing favorable opportunities to obtain high-resolution pre-event images. In this study, we propose an unsupervised change detection framework that uses post-fire VHR images with pre-fire PS data to facilitate the assessment of wildfire damage. To minimize the time and cost of human intervention, the entire process was executed in an unsupervised manner from image selection to change detection. First, to select clear pre-fire PS images, a blur kernel was adopted for the blind and automatic evaluation of local image quality. Subsequently, pseudo-training data were automatically generated from contextual features regardless of the statistical distribution of the data, whereas spectral and textural features were employed in the change detection procedure to fully exploit the properties of different features. The proposed method was validated in a case study of the 2019 Gangwon wildfire in South Korea, using post-fire GeoEye-1 (GE-1) and pre-fire PS images. The experimental results verified the effectiveness of the proposed change detection method, achieving an overall accuracy of over 99% with low false alarm rate (FAR), which is comparable to the accuracy level of the supervised approach. The proposed unsupervised framework accomplished efficient wildfire damage assessment without any prior information by utilizing the multiple features from multi-sensor bi-temporal images.
Highlights
Remote sensing techniques have been utilized to monitor ground surfaces in a broad range of fields, including disaster management
To perform rapid assessment of disaster damage, the acquisition of post-event very high resolution (VHR) images has been actively cooperated through cross-national programs such as the International Charter “Space and Major Disasters”’ initiative [1,2]
In the paper published by Wu et al [27], superpixel-based change detection was performed using training data that were determined by voting from five binary classification results, each derived from spectral, textural, and contextual features
Summary
Remote sensing techniques have been utilized to monitor ground surfaces in a broad range of fields, including disaster management. The aforementioned researchers produced the wildfire damage assessment results in high spatial resolution, these methods require manually generated samples to properly train the classifiers. In the paper published by Wu et al [27], superpixel-based change detection was performed using training data that were determined by voting from five binary classification results, each derived from spectral, textural, and contextual features. In both change detection approaches, thresholds are required to separate the region of change from the unchanged region; most of the widely used. (b) FigurFeig2u.reG2e.oGEeyoeE-y1e(-G1 E(G-1E)-1i)mimagaegsesacaqcquuiirreedd oveerr ((aa))GGaannggnneuenugn–gE–aEstaSsetaSreeagrioengiaonnda(nb)dG(obs)eGonogs–eong– SokchSokrcehgoiornegiinonGiannGgwanognwpornovpirnocvein, cSeo,uStohuKthoKreoareoanoAnpArpilr7il, 72,021091,9d, idsipslpalyayededininfafalslsee--ccoolloorr iimmagee with near-winifthranreeadr-(iNnfIrRar)e, dre(dN,IaRn),dregdr,eaenndbgarneedns.bands
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