Evaluating SAR-Optical Image Fusions for Urban LULC Classification in Vancouver Canada

Werner, Adrian; Storie, Christopher D.; Storie, Joni

doi:10.1080/07038992.2014.976700

Cited by 26 publications

(18 citation statements)

References 37 publications

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“…First, many researchers have combined SAR with multispectral data for urban land-cover mapping. Werner et al [ 29 ] combined RADARSAT-2 and SPOT-5 data for urban land cover classification and found that optical-radar image fusion improved classification accuracy. Shao et al [ 30 ] combined the GF-1 multispectral and Sentinel-1A data for urban impervious surface extraction at decision level, with an overall accuracy of 95.33%.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

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Section: Introductionmentioning

confidence: 99%

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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“…In line with this, the study area was classified into five groups:buildup, tourism area, trading area, green urban area and agricultural area. In this study, supervised classification method was used for the land use classification (Werner et al, 2014;Johnson, Iizuka, 2016). Field data were collected using GPS to assess the classification accuracy.…”

Section: Land Usesmentioning

confidence: 99%

Seasonal Change and Spatial Distribution of Traffic Noise in Seferihisar, Turkey

Say¹,

Okten²,

AYSU>³

et al. 2017

Archives of Acoustics

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In this study, traffic noise in Seferihisar, which is a holiday resort by the Aegean Sea in the West of Turkey, was investigated. The noise occurring in summer and winter, on weekdays, and at weekends was mapped separately. Besides, land uses exposed to traffic noise were specified. In the carried out method, the land uses were primarily mapped by using the satellite images of the study area. Then, the noise was measured at 46 points during ten days in summer and winter, on weekdays and at weekends, and it was mapped by using Inverse Distance Weighting (IDW) method and by means of Geographic Information System (GIS) techniques. The measurements were taken in the daytime (07:00-19:00), in the evening (19:00-23:00), and at night (23:00-07:00) as stated in the Turkish Regulation of Assessment and Management of Environmental Noise. Since the time interval of the daytime measurements was large, the noise was measured in three different periods for the daytime measurements. In the formation of noise maps, logarithmic averages of all measurements taken at each point were used. The noise maps were overlapped with the land use maps by the help of the GIS techniques; the land use affected by the noise was analyzed.The results showed that the road traffic noise varied from summer to winter seasons in the study area depending on heavy or light traffic. The noise occurring in summer showed an increase both on weekdays and at weekends. While the limit value was exceeded in 49 of the measurement averages taken in winter, the limit value was exceeded in 96 measurements taken in summer. The noise maps formed according to the IDW method displayed that the areas in which the limit value was exceeded in the daytime in summer took up 14.27% of the study area while this rate went down to 4.06% in winter. The study area is one of the most important destinations for summer holiday. 20.12% of the area that exceeded the limit value in summer was builtup and 10.69% of them was tourism area. The noise together with population and traffic that increase in summer season is an important environmental problem against which some precautions should be taken. The findings of the study present significant results which might guide local governments in preventing and managing the noise.

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“…SAR datasets alone or optical datasets alone usually do not perform well in some applications [1,2]. However, the fusion of SAR and optical images can provide complementary information that is beneficial for the applications [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

Chen

Habib

et al. 2017

Remote Sensing

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Improving the matching reliability of multi-sensor imagery is one of the most challenging issues in recent years, particularly for synthetic aperture radar (SAR) and optical images. It is difficult to deal with the noise influence, geometric distortions, and nonlinear radiometric difference between SAR and optical images. In this paper, a method for SAR and optical images matching is proposed. First, interest points that are robust to speckle noise in SAR images are detected by improving the original phase-congruency-based detector. Second, feature descriptors are constructed for all interest points by combining a new Gaussian-Gamma-shaped bi-windows-based gradient operator and the histogram of oriented gradient pattern. Third, descriptor similarity and geometrical relationship are combined to constrain the matching processing. Finally, an approach based on global and local constraints is proposed to eliminate outliers. In the experiments, SAR images including COSMO-Skymed, RADARSAT-2, TerraSAR-X and HJ-1C images, and optical images including ZY-3 and Google Earth images are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method provides significant improvements in the number of correct matches and matching precision compared with the state-of-the-art SIFT-like methods. Near 1 pixel registration accuracy is obtained based on the matching results of the proposed method.

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Evaluating SAR-Optical Image Fusions for Urban LULC Classification in Vancouver Canada

Cited by 26 publications

References 37 publications

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Seasonal Change and Spatial Distribution of Traffic Noise in Seferihisar, Turkey

Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

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