Improving Ecotope Segmentation by Combining Topographic and Spectral Data

Radoux, Julien; Bourdouxhe, Axel; Coos, William; Dufrêne, Marc; Defourny, Pierre

doi:10.3390/rs11030354

Cited by 24 publications

(14 citation statements)

References 35 publications

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“…To obtain an error-free reference data set across resolutions, we considered a 2-m land cover map as ground truth (Fig. 2; Radoux et al, 2019). The original map covers the Walloon Region, Belgium, and includes ten land cover classes.…”

Section: Generating Sampling Units and Sub-samples From A Reference Lmentioning

confidence: 99%

How Response Designs and Class Proportions Affect the Accuracy of Validation Data

2020

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Reference data collected to validate land cover maps are generally considered free of errors. In practice, however, they contain errors despite all efforts to minimise them. These errors then propagate up to the accuracy assessment stage and impact the validation results. For photo-interpreted reference data, the three most widely studied sources of error are systematic incorrect labelling, vigilance drops, and demographic factors. How internal estimation errors, i.e., errors intrinsic to the response design, affect the accuracy of reference data is far less understood. In this paper, we analysed the impact of estimation errors for two types of legends (binary and multi-class) as well as for two common response designs (point-based and partition-based) with a range of sub-sample sizes. We showed that the accuracy of response designs depends on the class proportions within the sampling units, with complex landscapes being more prone to errors. As a result, response designs where the number of sub-samples are fixed are inefficient, and the labels of reference data sets have inconsistent confidence levels. In order to control estimation errors, to guarantee high accuracy standards of validation data, and to minimise data collection efforts, we proposed to rely on confidence intervals of the photo-interpreted data to define how many sub-samples should be labelled. In practice, sub-samples are iteratively selected and labelled until the estimated class proportions reach the desired level of confidence. As a result, less effort is spent on labelling obvious cases and the spared effort can be allocated to more complex cases, thus leading to an increased reliability of reference data and in turn better accuracy assessment. Across our study site, we demonstrated that such an approach could reduce the labelling effort by 50% to 75%, with greater gains in homogeneous landscapes. We contend that adopting this optimisation approach will not only increase the efficiency of reference data collection but will also help deliver reliable accuracy estimates to the user community.

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Section: Generating Sampling Units and Sub-samples From A Reference Lmentioning

confidence: 99%

How Response Designs and Class Proportions Affect the Accuracy of Validation Data

2020

Self Cite

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“…(3) Calculate the homogeneity of adjacent segments and their boundaries by Equations (7) and (8). Further, calculate local inter-segment and boundary homogeneities according to Equations (11) and (12).…”

Section: Segment Evaluationmentioning

confidence: 99%

“…GEOBIA framework typically encompasses several sub-procedures such as image segmentation, geo-object recognition, feature extraction and image classification [3][4][5][6][7][8][9]. Image segmentation relies on the spectral or spatial knowledge to produce a spatial partition with contiguous and homogeneous characteristics to form the basis or information carrier for the following processing steps [10,11]. Thus, image segmentation plays a crucial role to impact on the overall performance of the GEOBIA in geographic information science.…”

Section: Introductionmentioning

confidence: 99%

Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities

et al. 2019

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Image segmentation is extensively used in remote sensing spectral image processing. Most of the existing region merging methods assess the heterogeneity or homogeneity using global or pre-defined parameters, which lack the flexibility to further improve the goodness-of-fit. Recently, the local spectral angle (SA) threshold was used to produce promising segmentation results. However, this method falls short of considering the inherent relationship between adjacent segments. In order to overcome this limitation, an adaptive SA thresholds methods, which combines the inter-segment and boundary homogeneities of adjacent segment pairs by their respective weights to refine predetermined SA threshold, is employed in a hybrid segmentation framework to enhance the image segmentation accuracy. The proposed method can effectively improve the segmentation accuracy with different kinds of reference objects compared to the conventional segmentation approaches based on the global SA and local SA thresholds. The results of the visual comparison also reveal that our method can match more accurately with reference polygons of varied sizes and types.

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“…Beside the very high resolution airborne datasets, images from Sentinel-1 (10 m C-Band SAR data) and Sentinel-2 (10 and 20 m multispectral data) are used to analyze the temporal dynamic of the land cover. Finally, a 2 m open data land cover layer from the Lifewatch project (Radoux et al 2019) is used for training, as well as a vector database of roads, railways and rivers provided by the Walloon Region. This vector database was enhanced into a planimetric reference : a linear roads dataset and three planimetric datasets for railways, buildings and rivers.…”

Section: Definition Of User Needs and Objectivesmentioning

confidence: 99%

Creating Wallonia's New Very High Resolution Land Cover Maps: Combining Grass Gis Obia and Otb Pixel-Based Results

Lennert

Grippa

Radoux

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The Walloon region of Belgium has launched a research project that aims at elaborating a methodology for automated, high-quality land cover mapping, based primarily on its yearly 0.25m orthophoto coverage. Whereas in urban areas an object-based (OBIA) approach has been the privileged path in the last years as it allows taking into account shape information relevant for the characterization of man-made constructions, such an approach has its limits in the rural and more natural areas due to increased difficulties for segmentation and less sharp boundaries, thus calling for a pixel-based approach. The project thus consists in developing a combination of methods, and to integrate their results through an ensemble fusion approach. As many of the more natural land cover classes have temporal profiles which cannot be detected in a one-date orthoimage, Sentinel 1 and 2 data are also included in order to take advantage of their higher spectral and temporal resolution. All methods are trained using existing regional databases. In a second step, we combine the different LC classification results by fusioning them into one high-accuracy (over 90% OA) product, using a series of different approaches ranging from rule-based to machine learning to the Dempster-Shafer method. The entire toolchain is based on free and open source software, mainly GRASS GIS and Orfeo ToolBox. Results indicate the importance of the quality of the individual classifications for the fusion results and justify the choice of combining OBIA and pixel-based approaches in order to avoid the pitfalls of each.</p>

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Improving Ecotope Segmentation by Combining Topographic and Spectral Data

Cited by 24 publications

References 35 publications

How Response Designs and Class Proportions Affect the Accuracy of Validation Data

How Response Designs and Class Proportions Affect the Accuracy of Validation Data

Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities

Creating Wallonia's New Very High Resolution Land Cover Maps: Combining Grass Gis Obia and Otb Pixel-Based Results

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