Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution  Satellite Images

Byun, Young-Gi; Han, Youkyung; Chae, Tae-Byeong

doi:10.3390/rs70810347

Cited by 62 publications

(45 citation statements)

References 37 publications

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“…In recent literature, various algorithms for automatic change detection have been proposed, such as change vector analysis (CVA) [10], Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) [15], universal image quality index (UIQI) [16], and multivariate alteration detection (MAD) [17]. These algorithms have proven to be effective in many applications.…”

Section: Introductionmentioning

confidence: 99%

“…In the above methods, the difference image is created by subtracting the pixel value from multi-temporal images. The difference image is one of the main providers of change information, as it contains clues about spectral changes and has significantly large values associated with regions that show a high probability of being changed [16]. Finally, the threshold method is employed to identify the changed area by empirical strategies or statistical methods.…”

Section: Introductionmentioning

confidence: 99%

“…It measures the distortion between multi-temporal images with loss of correlation, luminance distortion, and contrast distortion. A piecewise correlation between two multispectral image data sets can provide valuable information regarding the location and characteristics of change [16]. The MAD method is based on a canonical correlation analysis (CCA) applied to change detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wang

Choi

et al. 2017

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Choi

et al. 2017

Remote Sensing

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“…In recent years, with the development and application of remote sensing technology, it has played an increasingly important role in natural resource surveying [3,4], dynamic monitoring [5,6], and natural surface water planning [7,8], thus attracting researchers' attention. Remote sensing images enable us to observe the earth from a totally different perspective and monitor its real-time changes.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery

Yang

Guo

Tan

et al. 2017

Water

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Abstract:The extraction of urban water bodies from high-resolution remote sensing images, which has been a hotspot in researches, has drawn a lot of attention both domestic and abroad. A challenging issue is to distinguish the shadow of high-rise buildings from water bodies. To tackle this issue, we propose the automatic urban water extraction method (AUWEM) to extract urban water bodies from high-resolution remote sensing images. First, in order to improve the extraction accuracy, we refine the NDWI algorithm. Instead of Band2 in NDWI, we select the first principal component after PCA transformation as well as Band1 for ZY-3 multi-spectral image data to construct two new indices, namely NNDWI1, which is sensitive to turbid water, and NNDWI2, which is sensitive to the water body whose spectral information is interfered by vegetation. We superimpose the image threshold segmentation results generated by applying NNDWI1 and NNDWI2, then detect and remove the shadows in the small areas of the segmentation results using object-oriented shadow detection technology, and finally obtain the results of the urban water extraction. By comparing the Maximum Likelihood Method (MaxLike) and NDWI, we find that the average Kappa coefficients of AUWEM, NDWI and MaxLike in the five experimental areas are about 93%, 86.2% and 88.6%, respectively. AUWEM exhibits lower omission error rates and commission error rates compared with the NDWI and MaxLike. The average total error rates of the three methods are about 11.9%, 18.2%, and 22.1%, respectively. AUWEM not only shows higher water edge detection accuracy, but it also is relatively stable with the change of threshold. Therefore, it can satisfy demands of extracting water bodies from ZY-3 images.

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“…With the continuous development of earth observation, very high resolution (VHR) satellite imaging plays a significant role in various applications, including image classification, change detection, object recognition, land-cover mapping, building extraction and urban planning [1][2][3][4][5]. Aspects such as the rich patterns, ground features and geometrical information of VHR satellite image classification form the basis to these applications.…”

Section: Introductionmentioning

confidence: 99%

GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification

Tao

Zhong

et al. 2017

Remote Sensing

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Abstract:Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing the classification accuracy from low-level features is often disregarded. We therefore proposed a two-stream deep-learning neural network strategy, with a main stream utilizing fine spatial-resolution panchromatic images to retain low-level information under a supervised residual network structure. An auxiliary line employed an unsupervised net to extract high-level abstract and discriminative features from multispectral images to supplement the spectral information in the main stream. Various feature extraction types from the neural network were selected and jointed in the novel net, as the combined high-and low-level features could provide a superior solution to image classification. In traditional convolutional neural networks, increased network depth might not influence the network performance perceptibly; however, we introduced a residual neural network to develop the expressive ability of the deeper net, increasing the role of net depth in feature extraction. To enhance feature robustness, we proposed a novel consolidation part in feature extraction. An adversarial net improved the feature extraction capabilities and aided digging the inherent and discriminative features from data, with increased extraction efficacy. Tests on satellite images indicated the high overall accuracy of our novel net, verifying that net depth or number of convolution kernels affected the classification capability. Various comparative tests proved the structural rationality for our two-stream structure.

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Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images

Cited by 62 publications

References 37 publications

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

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

Automated Extraction of Urban Water Bodies from ZY‐3 Multi‐Spectral Imagery

GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification

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