Quality assessment for geo‐spatial objects derived from remotely sensed data

Zhan, Qiwen; Molenaar, M.; Tempfli, K.; Shi, Wenzhong

doi:10.1080/01431160500057764

Cited by 169 publications

(138 citation statements)

References 9 publications

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“…However, only limited number of published works describes the area-based accuracy approach developed for GEOBIA [35]. Among the significant results in this field were studies done by Zhan et al [62] and Whiteside et al [63] who developed a framework for assessing the quality of geometric properties of image objects based on the error matrix idea. An area-based accuracy assessment (Table 4) was used to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality, user's accuracy, and producer's accuracy [62].…”

Section: Mangrove Composition Mapping Validationmentioning

confidence: 99%

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

2015

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Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping.

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Section: Mangrove Composition Mapping Validationmentioning

confidence: 99%

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

2015

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“…A False Negative (FN) is an entity corresponding to an object in the reference that is classified as background, and a False Positive (FP) is an entity classified as an object that does not correspond to an object in the reference. A True Negative (TN) is an entity belonging to the background both in the classification and in the reference (Heipke C et al, 1997) (Zhan Q et al, 2005). The confusion matrix has a very simple structure.…”

Section: Resultsmentioning

confidence: 99%

Automatic detection of surface water bodies from Sentinel-1 SAR images using Valley-Emphasis method

Duy

2016

TCCKHVTD

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Surface water resource plays as an important role in human daily life and in the eco-environment. In the study Valley-Emphasis method of automatic water extraction was employed to identify surface water bodies at three study areas, having different landscapes and covers, using Sentinel-1A IW images widely used automated Otsu method was performed for extracting surface water bodies to compare proposed method. The results of proposal method were compared to those of widely used Otsu method and the reference data (e.g. Lansat 7, 8) gave the highest Completeness (User accuracy), Correctness (Producer accuracy) and Quality (Overall accuracies) at 98.8%, 90.7 % and 89.7 %, respectively. The employed method is straightforward, easy to implement and may be applied for other areas even at regional or global scales. The method also improves automatic identification level of surface water bodies, providing essential information for flood disaster research.

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“…Similarly, the producer's accuracy (column values) was computed considering the agreement of a particular class to the summation of that column. In many cases, according to Zhan et al [105] and Tuermer et al [106], the user's accuracy represents a measure of correctness (Equation (9)), and the producer's accuracy represents as a measure of completeness (Equation (10)).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

et al. 2016

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Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ě λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes.

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Quality assessment for geo‐spatial objects derived from remotely sensed data

Cited by 169 publications

References 9 publications

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

Automatic detection of surface water bodies from Sentinel-1 SAR images using Valley-Emphasis method

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

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