Learning to Match Aerial Images with Deep Attentive Architectures

Altwaijry, Hani; Trulls, Eduard; Hays, James; Fua, Pascal; Belongie, Serge

doi:10.1109/cvpr.2016.385

Cited by 72 publications

(54 citation statements)

References 36 publications

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“…Another possible reason is the lack of local information targeting modules. It could be optimized using additional layers focusing on spatial transform and patch or object level processing [1,21].…”

Section: Methodsmentioning

confidence: 99%

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Learning Near Duplicate Image Pairs using Convolutional Neural Networks

Zhang¹,

Zhang²,

Sun³

et al. 2018

IJPE

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In this paper, we illustrate how to learn a general straightforward similarity function from raw image pairs, which is a fundamental task in computer vision. To encode the function, inspired by the recent achievements of deep learning methods, we explore several deep neural networks and adopt one of the suitable networks to our task encoding implementation with several models on benchmark datasets UKBench and Holidays. The adopted network achieves comparable overall results and especially presents the excellent learning ability for global-similar data. Compared to previous approaches, this function eliminates the complex handcrafted features extraction, and utilizes pairwise correlation information by the jointly processing.

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Section: Methodsmentioning

confidence: 99%

“…To sum up, the contributions of this paper are as follow: (1) We learn an image pair-wise similarity jointly learning function without any handcrafted features. (2) We explore various neural networks and propose a network model to represent the function.…”

Section: Introductionmentioning

confidence: 99%

Learning Near Duplicate Image Pairs using Convolutional Neural Networks

Zhang¹,

Zhang²,

Sun³

et al. 2018

IJPE

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show abstract

“…Such techniques include graph matching and image segmentation. Another promising solution could be represented by deep learning approaches as reported in (Altwaijry et al 2016). …”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

Accurate Estimation of Orientation Parameters of Uav Images Through Image Registration With Aerial Oblique Imagery

Onyango

Nex

Peter

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Unmanned Aerial Vehicles (UAVs) have gained popularity in acquiring geotagged, low cost and high resolution images. However, the images acquired by UAV-borne cameras often have poor georeferencing information, because of the low quality on-board Global Navigation Satellite System (GNSS) receiver. In addition, lightweight UAVs have a limited payload capacity to host a high quality on-board Inertial Measurement Unit (IMU). Thus, orientation parameters of images acquired by UAV-borne cameras may not be very accurate. Poorly georeferenced UAV images can be correctly oriented using accurately oriented airborne images capturing a similar scene by finding correspondences between the images. This is not a trivial task considering the image pairs have huge variations in scale, perspective and illumination conditions. This paper presents a procedure to successfully register UAV and aerial oblique imagery. The proposed procedure implements the use of the AKAZE interest operator for feature extraction in both images. Brute force is implemented to find putative correspondences and later on Lowe's ratio test (Lowe, 2004) is used to discard a significant number of wrong matches. In order to filter out the remaining mismatches, the putative correspondences are used in the computation of multiple homographies, which aid in the reduction of outliers significantly. In order to increase the number and improve the quality of correspondences, the impact of pre-processing the images using the Wallis filter (Wallis, 1974) is investigated. This paper presents the test results of different scenarios and the respective accuracies compared to a manual registration of the finally computed fundamental and essential matrices that encode the orientation parameters of the UAV images with respect to the aerial images.

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“…Determining the camera pose in real time, in indoor environments, is practicable by CAD model matching (Ulrich et al, 2009;Zang and Hashimoto, 2011;Urban et al, 2013;Mueller and Voegtle, 2016). Convolutional neural networks are used to determine matches between aerial images and UAV images (Altwaijry et al, 2016) or terrestrial images and UAV images (Lin et al, 2015). However these methods are not operable on small drones in large environments, due to the on-board computers limited storage.…”

Section: Related Workmentioning

confidence: 99%

Squeezeposenet: Image Based Pose Regression With Small Convolutional Neural Networks for Real Time Uas Navigation

Müller

Urban

Jutzi

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.

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Learning to Match Aerial Images with Deep Attentive Architectures

Cited by 72 publications

References 36 publications

Learning Near Duplicate Image Pairs using Convolutional Neural Networks

Learning Near Duplicate Image Pairs using Convolutional Neural Networks

Accurate Estimation of Orientation Parameters of Uav Images Through Image Registration With Aerial Oblique Imagery

Squeezeposenet: Image Based Pose Regression With Small Convolutional Neural Networks for Real Time Uas Navigation

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