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Topic

Aerial image

About: Aerial image is a(n) research topic. Over the lifetime, 3326 publication(s) have been published within this topic receiving 42035 citation(s).


Papers
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Journal ArticleDOI
Abstract: Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.

666 citations

Proceedings ArticleDOI
01 Jun 2018
Abstract: Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect 2806 aerial images from different sensors and platforms. Each image is of the size about 4000 A— 4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using 15 common object categories. The fully annotated DOTA images contains 188, 282 instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral. To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.

587 citations

Journal ArticleDOI
TL;DR: The Aerial Image data set (AID), a large-scale data set for aerial scene classification, is described to advance the state of the arts in scene classification of remote sensing images and can be served as the baseline results on this benchmark.
Abstract: Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in remote sensing area and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing datasets for aerial scene classification like UC-Merced dataset and WHU-RS19 are with relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image Dataset (AID): a large-scale dataset for aerial scene classification. The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than ten thousands aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely-used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.

572 citations

Proceedings ArticleDOI
18 Jun 1996
TL;DR: A new space-sweep approach to true multi-image matching is presented that simultaneously determines 2D feature correspondences and the 3D positions of feature points in the scene.
Abstract: The problem of determining feature correspondences across multiple views is considered. The term "true multi-image" matching is introduced to describe techniques that make full and efficient use of the geometric relationships between multiple images and the scene. A true multi-image technique must generalize to any number of images, be of linear algorithmic complexity in the number of images, and use all the images in an equal manner. A new space-sweep approach to true multi-image matching is presented that simultaneously determines 2D feature correspondences and the 3D positions of feature points in the scene. The method is illustrated on a seven-image matching example from the aerial image domain.

565 citations

BookDOI
01 Aug 1995
Abstract: General Topics and Scene Reconstruction- An Overview of DARPA's Research Program in Automatic Population of Geospatial Databases- A Testbed for the Evaluation of Feature Extraction Techniques in a Time Constrained Environment- The Role of Artificial Intelligence in the Reconstruction of Man-Made Objects from Aerial Images- Scene Reconstruction Research - Towards an Automatic System- Semantic Modelling of Man-Made Objects by Production Nets- From Large-Scale DTM Extraction to Feature Extraction- Building Detection and Reconstruction- 3-D Building Reconstruction with ARUBA: A Qualitative and Quantitative Evaluation- A System for Building Detection from Aerial Images- On the Reconstruction of Urban House Roofs from Aerial Images- Image-Based Reconstruction of Informal Settlements- A Model Driven Approach to Extract Buildings from Multi-View Aerial Imagery- Automated Building Extraction from Digital Stereo Imagery- Application of Semi-Automatic Building Acquisition- On the Integration of Object Modeling and Image Modeling in Automated Building Extraction from Aerial Images- TOBAGO - A Topology Builder for the Automated Generation of Building Models- Crestlines Constribution to the Automatic Building Extraction- Recognizing Buildings in Aerial Image- Above-Ground Objects in Urban Scenes from Medium Scale Aerial Imagery- Digital Surface Models for Building Extraction- Extracting Artificial Surface Objects from Airborne Laser Scanner Data- Interpretation of Urban Surface Models using 2D Building Information- Least Squares Matching for Three Dimensional Building Reconstruction- Assessment of the Effects of Resolution on Automated DEM and Building Extraction- Road Extraction- The Role of Grouping for Road Extraction- Artificial Intelligence in 3-D Feature Extraction- Updating Road Maps by Contextual Reasoning- Fast Robust Tracking of Curvy Partially Occluded Roads in Clutter in Aerial Images- Linear Feature Extraction with 3-D LSB-Snakes- Context-Supported Road Extraction- Map/GIS-Based Methods- Three-Dimensional Description of Dense Urban Areas using Maps and Aerial Images- MOSES: A Structural Approach to Aerial Image Understanding- An Approach for the Extraction of Settlement Areas- Extraction of Polygonal Features from Satellite Images for Automatic Registration: The ARCHANGEL Project- Visualisation- A Set of Visualization Data Needs in Urban Environmental Planning & Design for Photogrammetric Data- A Virtual Reality Model of a Major International Airport- Managing Large 3D Urban Database Contents Supporting Phototexture and Levels of Detail- List of Workshop Participants- Author Index

514 citations

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20223
2021156
2020252
2019267
2018208
2017184