PERPUSTAKAAN BIG

1. Center pivot field delineation and mapping: A satellite-driven object-based image analysis approach for national scale accounting p. 1-19
2. Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation p. 20-33
3. Refinement of interferometric SAR parameters using digital terrain model as an external reference p. 34-43
4. Floorplan generation from 3D point clouds: A space partitioning approach p. 44-55
5. Structure-aware completion of photogrammetric meshes in urban road environment p. 56-70
6. Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States p. 71-87
7. Regional evaluation of satellite-based methods for identifying leaf unfolding date p. 88-98
8. Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method p. 99-118
9. Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework p. 119-131
10. Learning from multimodal and multitemporal earth observation data for building damage mapping p. 132-143
11. Mapping trees along urban street networks with deep learning and street-level imagery p. 144-157
12. Artificial and beneficial – Exploiting artificial images for aerial vehicle detection p. 158-170
13. Mapping crop types in complex farming areas using SAR imagery with dynamic time warping p. 171-183
14. DDRNet: Fast point cloud registration network for large-scale scenes p. 184-198
15. A point-based deep learning network for semantic segmentation of MLS point clouds p. 199-214
16. Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine p. 215-235
17.Greenness, texture, and spatial relationships predict floristic diversity across wetlands of the conterminous United States p. 236-246
18. CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery p. 247-267
19. Metrics for aerial, urban lidar point clouds p. 268-281
20. Validation and consistency assessment of land surface temperature from geostationary and polar orbit platforms: SEVIRI/MSG and AVHRR/Metop p. 282-297
21. Detecting individual abandoned houses from google street view: A hierarchical deep learning approach p. 298-310
22. Estimating daily full-coverage near surface O3, CO, and NO2 concentrations at a high spatial resolution over China based on S5P-TROPOMI and GEOS-FP p. 311-325
23. Individual tree extraction from urban mobile laser scanning point clouds using deep pointwise direction embedding p. 326-339
24. GAMSNet: Globally aware road detection network with multi-scale residual learning p. 340-352
25. A Global Context-aware and Batch-independent Network for road extraction from VHR satellite imagery p. 353-365
26. Registration of multi-platform point clouds using edge detection for rockfall monitoring p. 366-385
27. Detecting inconsistent information in crowd-sourced street networks based on parallel carriageways identification and the rule of symmetry p. 386-402
28. Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery p. 403-415
29. Efficient interactions for reconstructing complex buildings via joint photometric and geometric saliency segmentation p. 416-430
30. A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages p. 431-447
31. Attention aware cost volume pyramid based multi-view stereo network for 3D reconstruction p. 448-460

M.4593-12-2021

526.982

Perpustakaan BIG

Tersedia

• Center pivot field delineation and mapping: A satellite-driven object-based image analysis approach for national scale accounting
• Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation
• Refinement of interferometric SAR parameters using digital terrain model as an external reference
• Floorplan generation from 3D point clouds: A space partitioning approach
• Structure-aware completion of photogrammetric meshes in urban road environment
• Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States
• Regional evaluation of satellite-based methods for identifying leaf unfolding date
• Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method
• Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework
• Learning from multimodal and multitemporal earth observation data for building damage mapping
• Mapping trees along urban street networks with deep learning and street-level imagery
• Artificial and beneficial – Exploiting artificial images for aerial vehicle detection
• Mapping crop types in complex farming areas using SAR imagery with dynamic time warping
• DDRNet: Fast point cloud registration network for large-scale scenes
• A point-based deep learning network for semantic segmentation of MLS point clouds
• Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine
• Greenness, texture, and spatial relationships predict floristic diversity across wetlands of the conterminous United States
• CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery
• Metrics for aerial, urban lidar point clouds
• Validation and consistency assessment of land surface temperature from geostationary and polar orbit platforms: SEVIRI/MSG and AVHRR/Metop

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