Polarimetric classification of land cover for Glen Affric radar project

Lumsdon, Parivash; Cloude, S.R.; Wright, G. M.

doi:10.1049/ip-rsn:20041313

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…Obtained results compare favourably to previous similar studies on land cover classification with SAR data [83,20,21,41,28,31]. Depending on the level of classes aggregation (4-5 major classes or more), with using mostly statistical or classical machine learning approaches reported classification accuracies were as high as 80-87% to as low as 30% when only SAR imagery were used.…”

Section: Comparison To Similar Worksupporting

confidence: 78%

“…Second, the CORINE map itself does not have a perfect accuracy, neither aggregation rules are perfect. As a matter of fact, in majority of studies where SAR based classification was done versus CLC or similar data, a poor or modest overall agreement was observed for this class [21,41,83,20], while the user's accuracy was strongly higher than producer's [104]. The latter is exactly due to radar being able to sense sharp boundaries and bright targets very well whereas such bright targets often don't dominate the whole CORINE Level-1 urban class.…”

Section: Classification Performancementioning

confidence: 99%

“…When using exclusively SAR data for land cover mapping, reported accuracy often turn out to be relatively low for operational land cover mapping and change monitoring. Methodologically, reported solutions utilized supervised approaches, linking SAR observables and class labels to pixels, superpixels or objects in parametric or nonparametric manner [20,21,33,31,19,34,35,36,37,38,39,40,41].…”

Section: Land Cover Mapping With Sar Imagerymentioning

confidence: 99%

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Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models

Šćepanović¹,

Antropov²,

Laurila³

et al. 2019

Preprint

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Land cover mapping is essential for monitoring the environment and understanding the effects of human activities on it. The automatic approaches to land cover mapping (i.e., image segmentation) mostly used traditional machine learning that requires heuristic feature design. On the natural images, deep learning has outperformed traditional machine learning approaches on a range of tasks, including the image segmentation. On remote sensing images, recent studies are demonstrating successful application of specific deep learning models or their adaptations to particular small-scale land cover mapping tasks (e.g., to classify wetland complexes). However, it is not readily clear which of the existing state-of-the-art models for natural images are the best candidates to be taken for the particular remote sensing task and data.In this study, we answer that question for mapping the fundamental land cover classes using the satellite imaging radar data. We took ESA Sentinel-1 Cband SAR images available at no cost to users as representative data. CORINE land cover map produced by the Finnish Environment Institute was used as a reference, and the models were trained to distinguish between the 5 Level-1 CORINE classes. We selected seven among the state-of-the-art semantic segmentation models so that they cover a diverse set of approaches: U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and FRRN-B. These models were pre-trained on the ImageNet dataset and further fine-tuned in this study. Specifically, we used 14 ESA Sentinel-1 scenes acquired during the summer season in Finland, which are representative of the land cover in the country.Upon the evaluation and benchmarking, all the models demonstrated solid performance. The best model, FC-DenseNet (Fully Convolutional DenseNets), achieved the overall accuracy of 90.7%. Except for the producer accuracy of two classes (urban and water bodies), FC-DenseNet has outperformed all the other models across the accuracy measures and the classes. Overall, our results indicate that the semantic segmentation models are suitable for efficient widearea mapping using satellite SAR imagery. Our results also provide baseline accuracy against which the newly proposed models should be evaluated and

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Section: Comparison To Similar Worksupporting

confidence: 78%

Section: Classification Performancementioning

confidence: 99%

Section: Land Cover Mapping With Sar Imagerymentioning

confidence: 99%

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Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models

Šćepanović¹,

Antropov²,

Laurila³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Obtained results compare favourably to previous similar studies on land cover classification with SAR data [20], [28], [31], [42], [75], [99]. Depending on the level of classes aggregation (4-5 major classes or more), using mostly statistical or classical machine learning approaches reported classification accuracies were as high as 80-87% to as low as 30% when only SAR imagery were used.…”

Section: Comparison To Similar Worksupporting

confidence: 77%

“…Second, the CORINE map itself does not have perfect accuracy, neither aggregation rules are perfect. As a matter of fact, in a majority of studies where SAR based classification was done versus CLC or similar data, a poor or modest overall agreement was observed for urban land use areas [20], [42], [75], [99], while the user's accuracy was strongly higher than producer's [21]. The latter is exactly due to radar being able to sense sharp boundaries and bright targets very well whereas such bright targets often don't dominate the whole urban land-use class.…”

Section: A Classification Performancementioning

confidence: 99%

Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models

Šćepanović¹,

Antropov²,

Laurila³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land cover (LC) mapping is essential for monitoring the environment and understanding the effects of human activities on it. Recent studies demonstrated successful applications of specific deep learning models to small-scale LC mapping tasks (e.g., wetland mapping). However, it is not readily clear which of the existing state-of-the-art models for natural images are the best candidates to be taken for the particular remote sensing task and data. In this study, we answer that question for mapping the fundamental LC classes using the satellite imaging radar data. We took ESA Sentinel-1 C-band SAR images acquired during the whole summer season of 2018 in Finland, which are representative of the land cover in the country. CORINE LC map was used as a reference, and the models were trained to distinguish between the 5 major CORINE based classes. We selected seven among the state-of-the-art semantic segmentation models so that they cover a diverse set of approaches: U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and FRRN-B, and further fine-tuned them. Upon evaluation and benchmarking, all the models demonstrated solid performance with overall accuracy between 87.9% and 93.1%, with good to a very good agreement (kappa statistic between 0.75 and 0.86). The two best models were FC-DenseNet (Fully Convolutional DenseNets) and SegNet (Encoder-Decoder-Skip), with the latter having a much shorter inference time. Overall, our results indicate that the semantic segmentation models are suitable for efficient wide-area mapping using satellite SAR imagery and provide baseline accuracy against which the newly proposed models should be evaluated.

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Classification of forest composition using polarimetric decomposition in multiple landscapes

Dickinson

Siqueira

Clewley

et al. 2013

Remote Sensing of Environment

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Polarimetric classification of land cover for Glen Affric radar project

Cited by 12 publications

References 6 publications

Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models

Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models

Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models

Classification of forest composition using polarimetric decomposition in multiple landscapes

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