ISPRS Journal of Photogrammetry and Remote Sensing Vol.156 October 2019
1.TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation p.1-13 2.Mapping dead forest cover using a deep convolutional neural network and digital aerial p…
- Edisi
- Vol.156
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 259hlm.: illus 28cm
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetry and Remote Sensing Vol.157 November 2019
1.Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation p.1-12 2.Optimizing multiscale segmentation with local spectral heterogeneity mea…
- Edisi
- Vol.157
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 215 hlm.: illus 25cm
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetry and Remote Sensing Vol. 170 December 2020
1. Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area p. 1-14 2. Parsing very high resolution urban scene images by learning deep ConvNets with ed…
- Edisi
- Volume 170 December 2020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 215 hlm. : illus. ; 26 cm.
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetry and Remote Sensing Volume 169 November 2020
1. Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets p. 116 2. Retrieving snow wetness based on surface and volume scattering simulation 17-…
- Edisi
- Volume 169 November 20020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 435 hlm. :illus. ;28 cm.
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetry and Remote Sensing Volume 167 September 2020
1. A low-cost UAV framework towards ornamental plant detection and counting in the wild p.1-11 2. X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data p…
- Edisi
- Volume 167 September 2020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 457 hlm. : illus. ; 28 cm.
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetryand Remote Sensing Volume 168 October 2020
1. See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning p. 1-16 2. Pruning multi-view ste…
- Edisi
- Volume 168 October 2020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 306 hlm. : illus. ; 28 cm.
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetry and Remote Sensing Volume 163 May 2020
1. A label-noise robust active learning sample collection method for multi-temporal urban land-cover classification and change analysisp. 1-17 2. Robust normal vector estimation in 3D point clouds…
- Edisi
- Volume 163 May 2020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 389 hlm. : illus. ; 28 cm,
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetry and Remote Sensig Volume 164 June 2020
1. Photogrammetric determination of 3D crack opening vectors from 3D displacement fields p. 1-10 2. National wetland mapping in China: A new product resulting from object-based and hierarchical cl…
- Edisi
- Volume 164 June 2020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 242 hlm. : illus. ; 28 cm.
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetry and Remote Sensing Volume 166, August 2020
1. HyNet: Hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery p. 1-14 2. Near-real time forecasting and change detection for an open ecosystem wit…
- Edisi
- Volume 166 August 2020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 402 hlm. : illus. ; 28 cm.
- Judul Seri
- -
- No. Panggil
- 526.982
ISPRS Journal of Photogrammetri and Remote Sensing Volume 161 March 2020
1. Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data p. 1-12 2. 3D map-guided single indoor image localization refinemen…
- Edisi
- Volume 161 March 2020
- ISBN/ISSN
- 0924-2716
- Deskripsi Fisik
- 322 hlm. : illus. ; 28 cm.
- Judul Seri
- -
- No. Panggil
- 526.982
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