A Saliency Detector for Polarimetric SAR Ship Detection Using Similarity Test

Cui, Xing-Chao; Su, Yi; Chen, Siwei

doi:10.1109/jstars.2019.2925833

Cited by 35 publications

(24 citation statements)

References 48 publications

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“…These surface-based reconstruction methods [9,15,[22][23][24][25][26][27][28][29][30][31] mostly depend on the accuracy of surface extraction. In complex indoor scenes, the efficiency of surface extraction is low and contains excessive noise.…”

Section: Discussionmentioning

confidence: 99%

“…(In Figure 2c, an extracted opening as the template that encircled in red; in Figure 2d, the identified doors are shaded in green). Compared with the previous method [31], the extracted opening boundaries are visually more defined. As for the extraction of pillars, the technique is similar to extracting openings.…”

Section: Detection Of Openingsmentioning

confidence: 97%

“…Current methods for the reconstruction of indoor spaces are mainly based on the extraction of surfaces [9,15,[22][23][24][25][26][27][28][29][30][31] and lines [32][33][34][35][36][37][38][39][40].…”

Section: Reconstruction Of Indoor Spacementioning

confidence: 99%

“…Since openings are generally attached to wall surfaces and as holes in the point clouds of wall surfaces, the extraction of doors and windows is based on the hierarchical relationship of plane-contour. The surfaces are first extracted based on the previous plane segmentation [31] (see wall surfaces illustrated in Figure 2a). The 3D point clouds of wall surfaces are projected into a 2D plane using the following conversion: while the global optimization of the indoor space is solved using the energy minimization function via multi-label graph cuts.…”

Section: Detection Of Openingsmentioning

confidence: 99%

“…The over-segmentation often occurs in the spatial partition of long corridors [13,14], creating a substantial challenge when segmenting long corridors. Neoteric methods [9,15,[22][23][24][25][26][27][28][29][30][31] have focused on extracting piecewise planar surfaces to construct the model. Although the results of room segmentation have been satisfactory, indoor models still are difficult to accurately reconstruct because of high levels of occlusion and noisy point cloud data, while still requiring interaction [22].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Structural 3D Reconstruction of Indoor Space for 5G Signal Simulation with Mobile Laser Scanning Point Clouds

Cui

Dong

2019

Remote Sensing

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3D modelling of indoor environment is essential in smart city applications such as building information modelling (BIM), spatial location application, energy consumption estimation, and signal simulation, etc. Fast and stable reconstruction of 3D models from point clouds has already attracted considerable research interest. However, in the complex indoor environment, automated reconstruction of detailed 3D models still remains a serious challenge. To address these issues, this paper presents a novel method that couples linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning. In our proposed approach, a fully automatic room segmentation is performed on the unstructured point clouds via multi-label graph cuts with semantic constraints, which can overcome the over-segmentation in the long corridor. Then, the horizontal slices of point clouds with individual room are projected onto the plane to form a binary image, which is followed by line extraction and regularization to generate floorplan lines. The 3D structured models are reconstructed by multi-label graph cuts, which is designed to combine segmented room, line and surface elements as semantic constraints. Finally, this paper proposed a novel application that 5G signal simulation based on the output structural model to aim at determining the optimal location of 5G small base station in a large-scale indoor scene for the future. Four datasets collected using handheld and backpack laser scanning systems in different locations were used to evaluate the proposed method. The results indicate our proposed methodology provides an accurate and efficient reconstruction of detailed structured models from complex indoor scenes.

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