| Deep learning in remote sensing applications: A meta-analysis and review |
146 |
| A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies |
99 |
| A new deep convolutional neural network for fast hyperspectral image classification |
72 |
| Classification with an edge: Improving semantic with boundary detection |
65 |
| Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning |
57 |
| A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform |
55 |
| Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks |
52 |
| Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data |
49 |
| Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective |
48 |
| A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification |
45 |
| Multi-scale object detection in remote sensing imagery with convolutional neural networks |
44 |
| Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification |
42 |
| UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras |
42 |
| PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval |
41 |
| MugNet: Deep learning for hyperspectral image classification using limited samples |
40 |
| Deep learning classifiers for hyperspectral imaging: A review |
40 |
| International benchmarking of terrestrial laser scanning approaches for forest inventories |
39 |
| Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks |
38 |
| Is field-measured tree height as reliable as believed A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest |
36 |
| Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning |
35 |
| A light and faster regional convolutional neural network for object detection in optical remote sensing images |
32 |
| Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors |
31 |
| Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification |
30 |
| Cloud removal in remote sensing images using nonnegative matrix factorization and error correction |
29 |
| A deep learning framework for remote sensing image registration |
29 |
| Building instance classification using street view images |
28 |
| A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches |
27 |
| A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning |
27 |
| A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification |
27 |
| Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China |
26 |
| Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network |
26 |
| Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges |
25 |
| Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations |
25 |
| Semantic labeling in very high resolution images via a self-cascaded convolutional neural network |
25 |
| A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem |
23 |
| Big earth observation time series analysis for monitoring Brazilian agriculture |
23 |
| Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network |
21 |
| Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands |
21 |
| Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform |
21 |
| Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery |
21 |
| Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel- Sentinel-2 and Landsat images |
21 |
| Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models |
21 |
| A multi-scale fully convolutional network for semantic labeling of 3D point clouds |
20 |
| Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture |
20 |
| Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction |
20 |
| Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification |
20 |
| Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands |
20 |
| Hyperspectral image classification via a random patches network |
19 |
| Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data |
19 |
| DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn |
19 |
