Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints

A new straight line matching method for aerial images is proposed in this paper. Compared to previous works, similarity constraints combining radiometric information in image and geometry attributes in object plane are employed in these methods. Firstly, initial candidate lines and the elevation values of lines projection plane are determined by corresponding points in neighborhoods of reference lines. Secondly, project reference line and candidate lines back forward onto the plane, and then similarity measure constraints are enforced to reduce the number of candidates and to determine the finial corresponding lines in a hierarchical way. Thirdly, "one-to-many" and "many-to-one" matching results are transformed into "one-to-one" by merging many lines into the new one, and the errors are eliminated simultaneously. Finally, endpoints of corresponding lines are detected by line expansion process combing with "image-object-image" mapping mode. Experimental results show that the proposed algorithm can be able to obtain reliable line matching results for aerial images.

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A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

Sensors ◽

10.3390/s19061317 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1317 ◽

Cited By ~ 5

Author(s):

Supu Xiu ◽

Yuanqiao Wen ◽

Haiwen Yuan ◽

Changshi Xiao ◽

Wenqiang Zhan ◽

...

Keyword(s):

Field Experiments ◽

Aerial Images ◽

Aerial Image ◽

Automatic Identification ◽

Identification System ◽

Accurate Identification ◽

Matching Algorithm ◽

Surface Vessels ◽

Aerial Vehicle ◽

Multi Level

In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel’s vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.

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An Anhydrous Bridge Recognition Algorithm in Aerial Image

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.734-737.3079 ◽

2013 ◽

Vol 734-737 ◽

pp. 3079-3084

Author(s):

Yin Wen Dong ◽

Luan Wan ◽

Zhao Ming Shi ◽

Jing Xin An

Keyword(s):

Key Words ◽

Recognition Algorithm ◽

Binary Image ◽

Aerial Images ◽

Aerial Image ◽

Geometric Features ◽

Original Image ◽

Line Extraction ◽

Regional Area ◽

Straight Line

Aiming at anhydrous bridge automatically identification in aerial images, an anhydrous bridge recognition algorithm based on the geometric characteristics is proposed. Firstly, the original image is do threshold segmentation to get binary image. Secondly, binary image is do morphological processed to get bridge area enhanced image and bridge area corrosion image, and these two bridge area are subtracted to extract suspected bridge area based on bridge rectangle feature. Finally, bridge regional area is positioned according to the straight-line characteristics of the bridge. Experimental results show the proposed algorithm can accurately identify the anhydrous bridge effectively. Key words: aerial image; anhydrous bridges identification; edge detection ; straight line extraction ; geometric features

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Extraction of Buildings from Multiple-View Aerial Images Using a Feature-Level-Fusion Strategy

Remote Sensing ◽

10.3390/rs10121947 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1947 ◽

Cited By ~ 5

Author(s):

Youqiang Dong ◽

Li Zhang ◽

Ximin Cui ◽

Haibin Ai ◽

Biao Xu

Keyword(s):

Point Clouds ◽

Aerial Images ◽

Aerial Image ◽

Detection Methods ◽

Building Detection ◽

Fusion Strategy ◽

Line Segments ◽

Straight Line ◽

Feature Level Fusion ◽

Level Fusion

Aerial images are widely used for building detection. However, the performance of building detection methods based on aerial images alone is typically poorer than that of building detection methods using both LiDAR and image data. To overcome these limitations, we present a framework for detecting and regularizing the boundary of individual buildings using a feature-level-fusion strategy based on features from dense image matching (DIM) point clouds, orthophoto and original aerial images. The proposed framework is divided into three stages. In the first stage, the features from the original aerial image and DIM points are fused to detect buildings and obtain the so-called blob of an individual building. Then, a feature-level fusion strategy is applied to match the straight-line segments from original aerial images so that the matched straight-line segment can be used in the later stage. Finally, a new footprint generation algorithm is proposed to generate the building footprint by combining the matched straight-line segments and the boundary of the blob of the individual building. The performance of our framework is evaluated on a vertical aerial image dataset (Vaihingen) and two oblique aerial image datasets (Potsdam and Lunen). The experimental results reveal 89% to 96% per-area completeness with accuracy above almost 93%. Relative to six existing methods, our proposed method not only is more robust but also can obtain a similar performance to the methods based on LiDAR and images.

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A2S-Det: Efficiency Anchor Matching in Aerial Image Oriented Object Detection

Remote Sensing ◽

10.3390/rs13010073 ◽

2020 ◽

Vol 13 (1) ◽

pp. 73

Author(s):

Zhifeng Xiao ◽

Kai Wang ◽

Qiao Wan ◽

Xiaowei Tan ◽

Chuan Xu ◽

...

Keyword(s):

Object Detection ◽

Ground Truth ◽

Adaptive Threshold ◽

Selection Method ◽

The Self ◽

Aerial Images ◽

Aerial Image ◽

Matching Method ◽

Aspect Ratios ◽

Oriented Object

Object detection is a challenging task in aerial images, where many objects have large aspect ratios and are densely arranged. Most anchor-based rotating detectors assign anchors for ground-truth objects by a fixed restriction of the rotation Intersection-over-Unit (IoU) between anchors and objects, which directly follow horizontal detectors. Due to many directional objects with a large aspect ratio, the object-anchor IoU is heavily influenced by the angle, which may cause few anchors assigned for some ground-truth objects. In this study, we propose an anchor selection method based on sample balance assigning anchors adaptively, which we name the Self-Adaptive Anchor Selection (A2S-Det) method. For each ground-truth object, A2S-Det selects a set of candidate anchors by horizontal IoU. Then, an adaptive threshold module is adopted on the set of candidate anchors, which calculates a boundary of these candidate anchors aiming to keep a balance between positive and negative anchors. In addition, we propose a coordinate regression of relative reference (CR3) module to precisely regress the rotating bounding box. We test our method on a public aerial image dataset, and prove better performance than many other one-stage detectors and two-stage detectors, achieving the mAP of 70.64. An efficiency anchor matching method helps the detector achieve better performance for objects with large aspect ratios.

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Dual-NMS: A Method for Autonomously Removing False Detection Boxes from Aerial Image Object Detection Results

Sensors ◽

10.3390/s19214691 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4691 ◽

Cited By ~ 1

Author(s):

Lin ◽

Wu ◽

Fu ◽

Wang ◽

Zhang ◽

...

Keyword(s):

Object Detection ◽

Removal Rate ◽

Aerial Images ◽

Aerial Image ◽

Post Processing ◽

False Detection ◽

Channel Separation ◽

Image Object Detection ◽

Image Object ◽

Detection Effect

In the field of aerial image object detection based on deep learning, it’s difficult to extract features because the images are obtained from a top-down perspective. Therefore, there are numerous false detection boxes. The existing post-processing methods mainly remove overlapped detection boxes, but it’s hard to eliminate false detection boxes. The proposed dual non-maximum suppression (dual-NMS) combines the density of detection boxes that are generated for each detected object with the corresponding classification confidence to autonomously remove the false detection boxes. With the dual-NMS as a post-processing method, the precision is greatly improved under the premise of keeping recall unchanged. In vehicle detection in aerial imagery (VEDAI) and dataset for object detection in aerial images (DOTA) datasets, the removal rate of false detection boxes is over 50%. Additionally, according to the characteristics of aerial images, the correlation calculation layer for feature channel separation and the dilated convolution guidance structure are proposed to enhance the feature extraction ability of the network, and these structures constitute the correlation network (CorrNet). Compared with you only look once (YOLOv3), the mean average precision (mAP) of the CorrNet for DOTA increased by 9.78%. Commingled with dual-NMS, the detection effect in aerial images is significantly improved.

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Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

Remote Sensing ◽

10.3390/rs11101157 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1157 ◽

Cited By ~ 8

Author(s):

Jorge Fuentes-Pacheco ◽

Juan Torres-Olivares ◽

Edgar Roman-Rangel ◽

Salvador Cervantes ◽

Porfirio Juarez-Lopez ◽

...

Keyword(s):

Precision Agriculture ◽

Image Data ◽

Ground Truth ◽

Aerial Images ◽

Aerial Image ◽

Data Set ◽

Visual Appearance ◽

Aerial Robots ◽

Lighting Conditions ◽

Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

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A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images

Remote Sensing ◽

10.3390/rs13142656 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2656

Author(s):

Furong Shi ◽

Tong Zhang

Keyword(s):

Distance Estimation ◽

Image Data ◽

Learning Technologies ◽

Aerial Images ◽

Superior Performance ◽

Aerial Image ◽

Great Success ◽

Building Extraction ◽

Shape Information ◽

Multi Scale

Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models.

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Road Detection Based on Edge Feature with GAC Model in Aerial Image

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142154032x ◽

2021 ◽

Author(s):

Linying Zhou ◽

Zhou Zhou ◽

Hang Ning

Keyword(s):

Transformation Method ◽

Aerial Images ◽

Aerial Image ◽

Initial Contour ◽

Detection Accuracy ◽

Road Detection ◽

Canny Operator ◽

The Road ◽

Gradient Magnitude ◽

Edge Feature

Road detection from aerial images still is a challenging task since it is heavily influenced by spectral reflectance, shadows and occlusions. In order to increase the road detection accuracy, a proposed method for road detection by GAC model with edge feature extraction and segmentation is studied in this paper. First, edge feature can be extracted using the proposed gradient magnitude with Canny operator. Then, a reconstructed gradient map is applied in watershed transformation method, which is segmented for the next initial contour. Last, with the combination of edge feature and initial contour, the boundary stopping function is applied in the GAC model. The road boundary result can be accomplished finally. Experimental results show, by comparing with other methods in [Formula: see text]-measure system, that the proposed method can achieve satisfying results.

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A MODIFIED MULTIPHASE LEVEL SET EVOLUTION SCHEME FOR AERIAL IMAGE SEGMENTATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005909 ◽

2007 ◽

Vol 21 (07) ◽

pp. 1195-1212 ◽

Cited By ~ 3

Author(s):

WANG WEI ◽

YANG XIN

Keyword(s):

Level Set ◽

Geometric Analysis ◽

Aerial Images ◽

Contourlet Transform ◽

Aerial Image ◽

Global Features ◽

Precise Shape ◽

Level Set Evolution ◽

Weighting Coefficients ◽

Evolution Scheme

This paper describes an innovative aerial images segmentation algorithm. The algorithm is based upon the knowledge of image multiscale geometric analysis using contourlet transform, which can extract the image's intrinsic geometrical structure efficiently. The contourlet transform is introduced to represent the most distinguished and the rotation invariant features of the image. A modified Mumford–Shah model is applied to segment the aerial image by a multifeature level set evolution. To avoid possible local minima in the level set evolution, we adjust the weighting coefficients of the multiscale features in different evolution periods, i.e. the global features have bigger weighting coefficients at the beginning stages which roughly define the shape of the contour, then bigger weighting coefficients are assigned to the detailed features for segmenting the precise shape. When the algorithm is applied to segment the aerial images with several classes, satisfied experimental results are achieved by the proposed method.

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