Qualifying the LIDAR-Derived Intensity Image as an Infrared Band in NDWI-Based Shoreline Extraction

Incekara, Abdullah Harun; Şeker, Dursun Zafer; Bayram, Bülent

doi:10.1109/jstars.2018.2875792

Cited by 17 publications

(6 citation statements)

References 31 publications

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“…Several factors lead to those measurements inaccuracies in the point clouds such as adverse weather conditions, e.g. fog [19]- [21] and rain [22], [23], objects reflective surface, the scanner itself [24]- [26], or by some pipelines that construct 3D objects from multi-view images [18], [27], [28]. Examples of those data inaccuracies can be seen in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Robust Object Classification Approach Using Spherical Harmonics

et al. 2022

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Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise or outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying 3D objects. The proposed framework first uses the voxel grid of concentric spheres to learn features over the unit ball. We then limit the spherical harmonics order level to suppress the effect of noise and outliers. In addition, the entire classification operation is performed in the Fourier domain. As a result, our proposed model learned features that are less sensitive to data perturbations and corruptions. We tested our proposed model against several types of data perturbations and corruptions, such as noise and outliers. Our results show that the proposed model has fewer parameters, competes with state-of-art networks in terms of robustness to data inaccuracies, and is faster than other robust methods. Our implementation code is also publicly available 1 .INDEX TERMS object recognition, point cloud classification, spherical harmonics, robust classification.

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Section: Introductionmentioning

confidence: 99%

Robust Object Classification Approach Using Spherical Harmonics

et al. 2022

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“…Then, the processed panchromatic and multi-spectral bands are fused and mosaicked to obtain a 1 × 1 m resolution image of the study area . Second, the Normalized Difference Water Index (NDWI) image was obtained by band calculation on the mosaicked image (Gao, 1996), and the water edge (coastline) was extracted from NDWI by the regional growing method (Liu et al, 2017;Incekara et al, 2018) (Figure 4A). Equation ( 3) is as follows:…”

Section: Experiments Study Area and Datamentioning

confidence: 99%

Coastline extraction based on multi-scale segmentation and multi-level inheritance classification

et al. 2022

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Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.

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“…The results of multi-resolution segmentation are mainly affected by scale parameters, shape, and compactness. Through experiment and error analysis, we set the segmentation scale, shape factor, and compactness factor as 10, 0.2, and 0.8, respectively (Benz et al, 2004;Flanders et al, 2003;Incekara et al, 2018). Then, on the basis of field investigation and image training area analysis, we constructed the decision tree rules (waterbody:NDWI > 0.45, woodland: NDVI > 0.20, constructed land: NDVI < 0, NDWI < 0.20, farmland: 0.20 > NDVI > 0, coastal aquaculture: 0.45 > NDWI > 0.20, bare land: NDSI < -0.05, Elevation > 50) (Incekara et al, 2018).…”

Section: Object-oriented Classificationmentioning

confidence: 99%

Spatiotemporal dynamics of coastal aquaculture and driving force analysis in Southeastern China

Ying

Valle

et al. 2020

Ecosyst Health Sustain

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Introduction: Large-scale disordered coastal aquaculture development causes severe environmental problems. However, quantitative assessments of spatiotemporal dynamics and driving factors for coastal aquaculture are essential and urgent for coastal aquaculture sustainability. Outcomes: Using remote sensing products and geographic information science, we investigated the long-term landscape changes due to coastal aquaculture, and explored its underlying driving factors in the Ningde coastal region, Southeastern China. Results show that coastal aquaculture area increased from 90.65 km 2 in 2003 to 96.08 km 2 in 2016, and its structure underwent tremendous changes. The area of artificial shrimp ponds increased by 496.15% and the area of farmland ponds decreased by 25.81% between 2003 and 2016. In addition, we revealed that from 2003 to 2016, the change trends of the coastal aquaculture area and the entire Ningde coastal region were consistent, and became more fragmented and dispersive. Furthermore, regression results indicate that the growth and attenuation of coastal aquaculture areas were significantly affected by the initial coastal aquaculture area in 2003. Discussion and Conclusion: To sustainably manage the coastal ecosystems, we provide several recommendations (e.g., a coupled human and natural systems approach to understanding human-nature interactions, integrated assessment, and systematical spatial planning and monitoring) for future research and management.

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Qualifying the LIDAR-Derived Intensity Image as an Infrared Band in NDWI-Based Shoreline Extraction

Cited by 17 publications

References 31 publications

Robust Object Classification Approach Using Spherical Harmonics

Robust Object Classification Approach Using Spherical Harmonics

Coastline extraction based on multi-scale segmentation and multi-level inheritance classification

Spatiotemporal dynamics of coastal aquaculture and driving force analysis in Southeastern China

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