Fusing high-resolution SAR and optical imagery for improved urban land cover study and classification

Abstract

The two objectives of this study are to compare the performances of different data fusion techniques for the enhancement of urban features and subsequently to improve urban land cover types classification using a refined Bayesian classification. For the data fusion, wavelet-based fusion, Brovey transform, Elhers fusion and principal component analysis are used and the results are compared. The refined Bayesian classification uses spatial thresholds defined from local knowledge and different features obtained through a feature derivation process. The result of the refined classification is compared with the results of a standard method and it demonstrates a higher accuracy. Overall, the research indicates that multi-source information can significantly improves the interpretation and classification of land cover types and the refined Bayesian classification is a powerful tool to increase the classification accuracy.

Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points

“…However, the SAR results improved when the spatial resolution of the data increased (the resolution was 4 m, 3 m or 1.5 m). Many studies using automatic classification methods deal with satellite images with lower spatial resolutions (e.g., [26][27][28][29][30]), but high-resolution aerial or satellite data have also been used [24,31,32]. Generally, prior studies suggest that the combined use of both optical and SAR data is useful because of the complementary nature of these datasets.…”

An object-oriented method based on multi-scale segmentation for classification and mapping from Quick bird images

“…At present, some progress has been made in this study area. In 1976, Ketting put forward the object-oriented method, and discussed one kind of segmentation algorithm ECHO [5] .Then, Benz, J. D. Watts, and Zhang Xiuying each elaborated the application of the method in their study, which get satisfactory classification results [6]. After 2008s, Ren Yuhuan provided available information for the detection of earthquake by using object-oriented method with Quick bird images [7].…”

Fuzzy B-spline optimization for urban slum three-dimensional reconstruction using ENVISAT satellite data

“…On the contrary, the same system can be used in combination with higher resolution satellites such as RADARSAT-2 SAR, TerraSAR X and airborne SAR systems for monitoring land use. Amarsaikhan et al [17] and Brook et al [18] have also shown how the combined use of optical and SAR data sets can enhance urban feature detection and enhancement.…”