Advanced Classification of Lands at TM and Envisat Images of Mongolia

Vandansambuu, Battsengel; Amarsaikhan, D.; Bat-Erdene, Ts.; Egshiglen, E.; Munkh-Erdene, A.; Ganzorig, M.

doi:10.4236/ars.2013.22014

Cited by 5 publications

(1 citation statement)

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“…As more and more algorithms and their improved versions have been used to fuse optical and SAR images, researchers have compared the performance of these methods for improving ground object interpretation. For instance, Battsengel et al compared the performance of intensity-hue-saturation (IHS) transform, Brovey transformation, and principal components analysis (PCA) in urban feature enhancement [4]. The analysis revealed that the images transformed through IHS have better characteristics in spectral and spatial separation of different urban levels.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Pixel-Level Fusion Algorithms and a New High-Resolution Dataset for SAR and Optical Image Fusion

Li,

Zhang,

Yang

et al. 2023

Remote Sensing

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Synthetic aperture radar (SAR) and optical images often present different geometric structures and texture features for the same ground object. Through the fusion of SAR and optical images, it can effectively integrate their complementary information, thus better meeting the requirements of remote sensing applications, such as target recognition, classification, and change detection, so as to realize the collaborative utilization of multi-modal images. In order to select appropriate methods to achieve high-quality fusion of SAR and optical images, this paper conducts a systematic review of current pixel-level fusion algorithms for SAR and optical image fusion. Subsequently, eleven representative fusion methods, including component substitution methods (CS), multiscale decomposition methods (MSD), and model-based methods, are chosen for a comparative analysis. In the experiment, we produce a high-resolution SAR and optical image fusion dataset (named YYX-OPT-SAR) covering three different types of scenes, including urban, suburban, and mountain. This dataset and a publicly available medium-resolution dataset are used to evaluate these fusion methods based on three different kinds of evaluation criteria: visual evaluation, objective image quality metrics, and classification accuracy. In terms of the evaluation using image quality metrics, the experimental results show that MSD methods can effectively avoid the negative effects of SAR image shadows on the corresponding area of the fusion result compared with CS methods, while model-based methods exhibit relatively poor performance. Among all of the fusion methods involved in the comparison, the non-subsampled contourlet transform method (NSCT) presents the best fusion results. In the evaluation using image classification, most experimental results show that the overall classification accuracy after fusion is better than that before fusion. This indicates that optical-SAR fusion can improve land classification, with the gradient transfer fusion method (GTF) yielding the best classification results among all of these fusion methods.

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Section: Introductionmentioning

confidence: 99%