Deep Learning in Remote Sensing
A Review of Researches on Deep Learning in Remote Sensing Application
... years, deep learning has been widely used in the field of image un- derstanding and made breakthroughs research progress in image under- ...Because remote sensing application and image ...
11
Deep learning for urban remote sensing
... how deep learning techniques can benefit to remote ...various deep network architectures and show that context information and dense labeling allow to reach better ...that deep ...
5
Deep Learning-Based Classification of Remote Sensing Image
... words: Deep Learning network (DLN), remote sensing image, ...resolution remote sensing image also brought coexistence of the opportunities and challenges to the classification ...
5
Deep learning decision fusion for the classification of urban remote sensing data
... spatial remote sensing data clas- sification were extensively investigated to enhance the classification performance by considering homogeneous areas as a set of neighboring pixels whose spectral ...
19
Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning
... optical remote sensing images are available in recent years, landslide recognition on optical remote sensing images is in ...a deep learning based landslide recognition method ...
10
Gradient regularisation increases deep learning performance under stress on remote sensing applications.
... of deep learning detectors have been designed. Most salient deep detectors are [20] which improves [6] but using deep learning box proposal instead of static one, [19] which directly ...
11
How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
... of deep learning approaches for computer vision and remote sensing is not an ...However, deep networks are not designed to directly process high resolution images such as the ones used ...
5
Modelling of Lake Water Quality Parameters by Deep Learning Using Remote Sensing Data
... R 2 =0.97 for the dissolved oxygen, R 2 =0.96 for the conductivity and R 2 =0.99 for the turbidity. Moreover, the root mean square error (RMSE) values for pH, dissolved oxygen, conductivity and turbidity were below 0.22, ...
7
Understanding Regional Ice Sheet Mass Balance: Remote Sensing, Regional Climate Models, and Deep Learning
... and deep learning provide additional ways to analyze vast quantities of visual remote sensing data to better understand the dynamics of ...
134
Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping
... two-stage deep learning fusion method, we demonstrate that fusion of single-band LiDAR data with HS image has sig- nificant improvement over current fusion methods even on fusion of full-waveform LiDAR and ...
14
remote sensing Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting Article
... 32. Horn, B.; Schunk, B. Determining Optical Flow. Artif. Intell. 1981, 17, 185–203. [CrossRef] 33. Rajkomar, A.; Oren, E.; Chen, K.; Hajaj, N.; Hardt, M.; Marcus, J.; Sundberg, P.; Yee, H.; Flores, G.; Sun, M.; et al. ...
21
Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics
... In this research, we have developed a novel method for complex feature learning from LiDAR data to improve land cover classification. The Voxnet architecture has several advantages that make it suitable for our ...
177
Investigations on Combinational Approach for Processing Remote Sensing Images Using Deep Learning Techniques
... Since the high-level TF API is Python, a light Python environment is provided to allow developers to build and train their models from patches images produced using our sampling application described in Section II-E1. As ...
6
Deep Learning Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion
... Mixed Shrub-Sedge Tussock Tundra-Bog Dryas/Lichen Dwarf Shrub Tundra Sedge-Willow-Dryas Tundra CNN Patch Based. CNN Patch Based Plot[r] ...
26
Mapping of urban landuse and landcover with multiple sensors : Joining close and remote sensing with deep learning
... In the last decade, great advances have been observed for the automation of land- cover maps using remote sensing imagery [Homer et al., 2015, Postadjian et al., 2017, Inglada et al., 2017] and current ...
130
Convolutional neural networks with deep supervised feature learning for remote sensing scene classification
... of remote sensing and numerous approaches have been ...the deep learning framework and most typically rely on Convolutional Neural Network architectures [1,8], employing categorical ...
17
SD-RSIC: Summarization-Driven Deep Remote Sensing Image Captioning
... the Remote Sensing Image Analysis (RSiM) group and pursuing the ...chine learning, with special interest in deep learning, large-scale image understanding and remote ...and ...
14
A Massively Parallel Deep Rule Based Ensemble Classifier for Remote Sensing Scenes
... Terms—deep learning, rule-based classifier, scene classification, fuzzy ...EMOTE sensing scene classification aims to allocate the sub-regions of fine spatial resolution images to distinct land use ...
5
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
... Contextual deep learning multinomial logistic regression (CDL-MLR) had a percent higher mean class accuracy for the second training ...self-taught learning frameworks, especially SCAE, proved to be ...
137
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
... To validate the performance of the proposed semi-supervised deep metric learning approach, this work considers two benchmark RS image archives. A detailed description of these datasets is provided below: 1. ...
18