Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests

Yu, Yongtao; Guan, Haiyan; Zai, Dawei; Zheng, Jianming

doi:10.1016/j.isprsjprs.2015.04.014

Cited by 37 publications

(15 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After matching cost thresholding, vehicle location estimation, and multiple detection results elimination, we obtained the final vehicle detection result. To quantitatively evaluate the accuracy and correctness of the vehicle detection results on the two UAV image datasets, we adopted the following four quantitative measures: completeness, correctness, quality, and F1-measure [41]. Completeness assesses the proportion of correctly detected vehicles with respect to the ground truth.…”

Section: Vehicle Detectionmentioning

confidence: 99%

“…In addition, multi-source data fusion strategies have also been explored and applied to vehicle detection recently [33,34].Deep learning techniques [35][36][37] have shown their superior advantages in mining hierarchical, high-level, distinctive feature representations. They have been widely used in a variety of applications, such as image segmentation [38,39], object detection [40,41], classification [42,43], image registration [44], etc. Consequently, vehicle detection by using deep learning techniques has also been intensively studied [45].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

Cao

Guan

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions.

show abstract

Section: Vehicle Detectionmentioning

confidence: 99%

mentioning

confidence: 99%

Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

Cao

Guan

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Aircraft detection is relatively mature compared with other targets. The many methods of aircraft detection can be roughly classified into two categories [20]: low-level features, such as edges and symmetry [21][22][23][24][25][26][27], and high-level features based on object features [20,[28][29][30][31][32][33]. For low-level features, Bo et al [21] converted RGB images to binary images for aircraft detection.…”

Section: Introductionmentioning

confidence: 99%

“…For high-level features, by increasing the dimensions of spectral features, textural features, or geometrical characteristics, classification stability is significantly improved [28,32,34,35]. Deep learning (DL) is the most prominent method that can automatically learn high-level features with high accuracy, and has created new ways to analyze remote sensing imagery [36].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

Zeng

Cheng

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Airports have a profound impact on our lives, and uncovering their distribution around the world has great significance for research and development. However, existing airport databases are incomplete and have a high cost of updating. Thus, a fast and automatic worldwide airport detection method can be of significance for global airport detection at regular intervals. However, previous airport detection studies are usually based on single remote sensing (RS) imagery, which seems an overwhelming burden for worldwide airport detection with traversal searching. Thus, we propose a hierarchical airport detection method consisting of broad-scale extraction of worldwide candidate airport regions based on spatial analysis of released RS products, including impervious surfaces from FROM-GLC10 (fine resolution observation and monitoring of global land cover 10) product, building distribution from OSMs (open street maps) and digital surface model from AW3D30 (ALOS World 3D—30 m). Moreover, narrow-scale aircraft detection was initially conducted by the Faster R-CNN (regional-convolutional neural networks) deep learning method. To avoid overestimation of background regions by Faster R-CNN, a second CNN classifier is used to refine the class labeling with negative samples. Specifically, our research focuses on target airports with at least 2 km length in three experimental regions. Results show that spatial analysis reduced the possible regions to 0.56% of the total area of 75,691 km2. The initial aircraft detection by Faster R-CNN had a mean user’s accuracy of 88.90% and ensured that all the aircrafts could be detected. Then, by introducing the CNN reclassifier, the user’s accuracy of aircraft detection was significantly increased to 94.21%. Finally, through an experienced threshold of aircraft number, 19 of the total 20 airports were detected correctly. Our results reveal the overall workflow is reliable for automatic and rapid airport detection around the world with the help of released RS products. This research promotes the application and progression of deep learning.

show abstract

“…Even though detection of airplanes on satellite images is not the main topic of this work, it is worth noting that it attracts considerable interest by itself [10,7,11,5,6,17]…”

Section: Related Workmentioning

confidence: 99%

Building Visual Anomaly Dataset from Satellite Data using ADS-B

Kastelic¹,

Perš²

EPiC Series in Computing

View full text Add to dashboard Cite

In this paper, we present a novel way of obtaining extremely challenging image dataset for the purpose of benchmarking image anomaly detection methods. By definition, anomalies are rare occurrences, and therefore, annotation of anomalies using human workforce is difficult and costly, as large amounts of mostly non-anomalous data need to be checked. To alleviate this problem, we use satellite images from Planet.com as the source of visual data, and combine them with ADS-B data to detect airplanes in a semi-automatic way. This way, our definition of anomaly is an appearance of an airplane on mostly airplane-free images. This not only speeds up annotation, but also provides the exact specification of what constitutes an anomaly, in an objective way. The resulting meta-dataset, containing references to Planet.com imagery and accurate annotations will be published in the near future. It will include locations of nearly 100 positions of airplanes on satellite images and the corresponding references to satellite images, captured in vicinity of large airports in different parts of the world, in different climate zones.

show abstract

Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests

Cited by 37 publications

References 50 publications

Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

Building Visual Anomaly Dataset from Satellite Data using ADS-B

Contact Info

Product

Resources

About