Categorizing highly complex aerial scenes is quite strenuous due to the presence of detailed information with large number of distinctive objects. Recognition happens by first deriving a joint relationship within all these distinguishing objects, distilling finally to some meaningful knowledge that is subsequently employed to label the scene. However, something intriguing is whether all this captured information is actually relevant to classify such a complex scene ? What if some objects just create uncertainty with respect to the target label, thereby causing ambiguity in the decision making ? In this paper, we investigate these questions and analyze as to which regions in an aerial scene are the most relevant and which are inhibiting in d...
Aerial scene classification is a challenging problem in understanding high-resolution remote sensing...
Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm...
This is the CITY-OSM dataset used in the journal publication "Learning Aerial Image Segmentation Fro...
Categorizing highly complex aerial scenes is quite strenuous due to the presence of detailed informa...
The advent of computer vision and evolution of high-end computing in remote sensing images have embe...
Compared with natural scenes, aerial scenes are usually composed of numerous objects densely distrib...
1087-1094The advent of computer vision and evolution of high-end computing in remote sensing images ...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
International audienceWe address the pixelwise classification of high-resolution aerial imagery. Whi...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
This thesis presents a brief introduction to aerial road detection and semantic segmentation of imag...
Scene classification relying on images is essential in many systems and applications related to remo...
When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution r...
Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial im...
Aerial scene classification is a challenging problem in understanding high-resolution remote sensing...
Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm...
This is the CITY-OSM dataset used in the journal publication "Learning Aerial Image Segmentation Fro...
Categorizing highly complex aerial scenes is quite strenuous due to the presence of detailed informa...
The advent of computer vision and evolution of high-end computing in remote sensing images have embe...
Compared with natural scenes, aerial scenes are usually composed of numerous objects densely distrib...
1087-1094The advent of computer vision and evolution of high-end computing in remote sensing images ...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
International audienceWe address the pixelwise classification of high-resolution aerial imagery. Whi...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
This thesis presents a brief introduction to aerial road detection and semantic segmentation of imag...
Scene classification relying on images is essential in many systems and applications related to remo...
When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution r...
Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial im...
Aerial scene classification is a challenging problem in understanding high-resolution remote sensing...
Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm...
This is the CITY-OSM dataset used in the journal publication "Learning Aerial Image Segmentation Fro...