ISPRS Journal of Photogrammetry and Remote Sensing Vol.156 October 2019

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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 photography p.14-26
3.A bridge-tailored multi-temporal DInSAR approach for remote exploration of deformation characteristics and mechanisms of complexly structured bridges p.27-50
4.Parameters determination and sensor correction method based on virtual CMOS with distortion for the GaoFen6 WFV camera p.51-62
5.Automatic canola mapping using time series of sentinel 2 images p.63-76
6.Spatial-spectral local discriminant projection for dimensionality reduction of hyperspectral image p.77-93
7.Automated fusion of forest airborne and terrestrial point clouds through canopy density analysis p.94-107
8.An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations p.108-120
9.Evaluation of the MODIS collections 5 and 6 for change analysis of vegetation and land surface temperature dynamics in North and South America p.121-134
10.Multi-sensor prediction of Eucalyptus stand volume: A support vector approach p.135-146
11.Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers p.147-159
12.Comparison of surface and canopy urban heat islands within megacities of eastern China p.160-168
13.Bundle adjustment of satellite images based on an equivalent geometric sensor model with digital elevation model p.169-183
14.Recovery of urban 3D road boundary via multi-source data p.184-201
15.Efficient and robust large-scale structure-from-motion via track selection and camera prioritization p.202-214
16.Random cross-observation intensity consistency method for large-scale SAR images mosaics: An example of Gaofen-3 SAR images covering China p.215-234
17.Multiple-view geospatial comparison using web-based virtual globes p.235-246
18.An adaptive machine learning approach to improve automatic iceberg detection from SAR images p.247-259
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No. Panggil



Elsevier : Amsterdam.,

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