scholarly journals Change Detection in Aerial Images Using Three-Dimensional Feature Maps

2020 ◽  
Vol 12 (9) ◽  
pp. 1404
Author(s):  
Saleh Javadi ◽  
Mattias Dahl ◽  
Mats I. Pettersson
Keyword(s):  
Change Detection ◽  
Sampling Rate ◽  
Three Dimensional ◽  
Image Resolution ◽  
Aerial Images ◽  
Aerial Image ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Feature Maps ◽  
Aerial Imaging

Interest in aerial image analysis has increased owing to recent developments in and availability of aerial imaging technologies, like unmanned aerial vehicles (UAVs), as well as a growing need for autonomous surveillance systems. Variant illumination, intensity noise, and different viewpoints are among the main challenges to overcome in order to determine changes in aerial images. In this paper, we present a robust method for change detection in aerial images. To accomplish this, the method extracts three-dimensional (3D) features for segmentation of objects above a defined reference surface at each instant. The acquired 3D feature maps, with two measurements, are then used to determine changes in a scene over time. In addition, the important parameters that affect measurement, such as the camera’s sampling rate, image resolution, the height of the drone, and the pixel’s height information, are investigated through a mathematical model. To exhibit its applicability, the proposed method has been evaluated on aerial images of various real-world locations and the results are promising. The performance indicates the robustness of the method in addressing the problems of conventional change detection methods, such as intensity differences and shadows.

scholarly journals 2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

2018 ◽  
Vol 10 (12) ◽  
pp. 2054 ◽  
Author(s):  
Veronika Gstaiger ◽  
Jiaojiao Tian ◽  
Ralph Kiefl ◽  
Franz Kurz
Keyword(s):  
Change Detection ◽  
Crisis Management ◽  
Large Scale ◽  
Three Dimensional ◽  
Aerial Imagery ◽  
Added Value ◽  
Detection Methods ◽  
Protestant Church ◽  
Existing Structures ◽  
3D Information

Large-scale events represent a special challenge for crisis management. To ensure that participants can enjoy an event safely and carefree, it must be comprehensively prepared and attentively monitored. Remote sensing can provide valuable information to identify potential risks and take appropriate measures in order to prevent a disaster, or initiate emergency aid measures as quickly as possible in the event of an emergency. Especially, three-dimensional (3D) information that is derived using photogrammetry can be used to analyze the terrain and map existing structures that are set up at short notice. Using aerial imagery acquired during a German music festival in 2016 and the celebration of the German Protestant Church Assembly of 2017, the authors compare two-dimensional (2D) and novel fusion-based 3D change detection methods, and discuss their suitability for supporting large-scale events during the relevant phases of crisis management. This study serves to find out what added value the use of 3D change information can provide for on-site crisis management. Based on the results, an operational, fully automatic processor for crisis management operations and corresponding products for end users can be developed.

scholarly journals Detection of Vehicular Traffic Using Convolutional Neural Networks

2019 ◽  
Vol 8 (10) ◽  
pp. 1423-1426
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Aerial Images ◽  
Aerial Image ◽  
Detection Methods ◽  
Design System ◽  
Detection Process ◽  
Present Generation ◽  
Successful Design

— In present generation the detection of vehicle using aerial images plays an important role and mot challenging. The video understanding, border security are the applications of aerial images. To improve the performance of the system different detection methods are introduced. But these methods take more time in detection process. To overcome these convolutional neural network are introduced which will produce the successful design system. the main intent of this paper is to present the recognition system for aerial images using convolutional neural network. The proposed method improves the accuracy and speed after the detection process. At last aerial image is obtained by matching the image and textual description of classes.

scholarly journals Axis Learning for Orientated Objects Detection in Aerial Images

2020 ◽  
Vol 12 (6) ◽  
pp. 908 ◽  
Author(s):  
Zhifeng Xiao ◽  
Linjun Qian ◽  
Weiping Shao ◽  
Xiaowei Tan ◽  
Kai Wang
Keyword(s):  
Computational Complexity ◽  
Aspect Ratio ◽  
Object Detection ◽  
Aerial Images ◽  
Aerial Image ◽  
Large Aspect Ratio ◽  
Computationally Efficient ◽  
Feature Maps ◽  
One Stage ◽  
Objects Detection

Orientated object detection in aerial images is still a challenging task due to the bird’s eye view and the various scales and arbitrary angles of objects in aerial images. Most current methods for orientated object detection are anchor-based, which require considerable pre-defined anchors and are time consuming. In this article, we propose a new one-stage anchor-free method to detect orientated objects in per-pixel prediction fashion with less computational complexity. Arbitrary orientated objects are detected by predicting the axis of the object, which is the line connecting the head and tail of the object, and the width of the object is vertical to the axis. By predicting objects at the pixel level of feature maps directly, the method avoids setting a number of hyperparameters related to anchor and is computationally efficient. Besides, a new aspect-ratio-aware orientation centerness method is proposed to better weigh positive pixel points, in order to guide the network to learn discriminative features from a complex background, which brings improvements for large aspect ratio object detection. The method is tested on two common aerial image datasets, achieving better performance compared with most one-stage orientated methods and many two-stage anchor-based methods with a simpler procedure and lower computational complexity.

scholarly journals RESOLUTION-AWARE NETWORK WITH ATTENTION MECHANISMS FOR REMOTE SENSING OBJECT DETECTION

2020 ◽  
Vol V-2-2020 ◽  
pp. 909-916
Author(s):  
Z. Tian ◽  
W. Wang ◽  
B. Tian ◽  
R. Zhan ◽  
J. Zhang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Attention Mechanism ◽  
Image Resolution ◽  
Uneven Distribution ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Feature Maps ◽  
Optical Remote Sensing Image ◽  
Comprehensive Evaluations

Abstract. Nowadays, deep-learning-based object detection methods are more and more broadly applied to the interpretation of optical remote sensing image. Although these methods can obtain promising results in general conditions, the designed networks usually ignore the characteristics of remote sensing images, such as large image resolution and uneven distribution of object location. In this paper, an effective detection method based on the convolutional neural network is proposed. First, in order to make the designed network more suitable for the image resolution, EfficientNet is incorporated into the detection framework as the backbone network. EfficientNet employs the compound scaling method to adjust the depth and width of the network, thereby meeting the needs of different resolutions of input images. Then, the attention mechanism is introduced into the proposed method to improve the extracted feature maps. The attention mechanism makes the network more focused on the object areas while reducing the influence of the background areas, so as to reduce the influence of uneven distribution. Comprehensive evaluations on a public object detection dataset demonstrate the effectiveness of the proposed method.

scholarly journals Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)

2021 ◽  
Vol 13 (23) ◽  
pp. 4918
Author(s):  
Te Han ◽  
Yuqi Tang ◽  
Xin Yang ◽  
Zefeng Lin ◽  
Bin Zou ◽  
...  
Keyword(s):  
Change Detection ◽  
Extreme Learning Machine ◽  
State Of The Art ◽  
Sample Selection ◽  
Training Sample ◽  
Google Earth ◽  
Detection Methods ◽  
Feature Maps ◽  
Optical Images ◽  
Learning Machine

To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.

scholarly journals COMPARISON OF PIXEL-BASED AND FEATURE-BASED APPROACH FOR SMALL OBJECT CHANGE DETECTION

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 353-357
Author(s):  
J. Seo ◽  
T. Kim
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Rate Of Change ◽  
Image Resolution ◽  
Detection Methods ◽  
Small Object ◽  
Feature Points ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Feature Based

Abstract. Satellite image resolution has evolved to daily revisit and sub-meter GSD. Main targets of previous remote sensing were forest, vegetation, damage area by disasters, land use and land cover. Developments in satellite images have brought expectations on more sophisticated and various change detection of objects. Accordingly, we focused on unsupervised change detection of small objects, such as vehicles and ships. In this paper, existing change detection methods were applied to analyze their performances for pixel-based and feature-based change of small objects. We used KOMPSAT-3A images for tests. Firstly, we applied two change detection algorithms, MAD and IR-MAD, which are most well-known pixel-based change detection algorithms, to the images. We created a change magnitude map using the change detection methods. Thresholding was applied to determine change and non-change pixels. Next, the satellite images were transformed as 8-bit images for extracting feature points. We extracted feature points using SIFT and SURF methods to analyze feature-based change detection. We assumed to remove false alarms by eliminating feature points of non-changed objects. Therefore, we applied a feature-based matcher and matched feature points on identical image locations were eliminated. We used non-matched feature points for change/non-change analysis. We observed changes by creating a 5x5 size ROI around extracted feature points in the change/non-change map. We determined that change has occurred on feature points if the rate of change pixels with ROI was more than 50%. We analyzed the performance of pixel-based and feature-based change detection using ground truths. The F1-score, AUC value, and ROC were used to compare the performance of change detection. Performance showed that feature-based approaches performed better than pixel-based approaches.

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

2018 ◽  
Vol 10 (12) ◽  
pp. 1947 ◽  
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.

scholarly journals Land use and land cover change detection and spatial distribution on the Tibetan Plateau

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuang Hao ◽  
Fengshun Zhu ◽  
Yuhuan Cui
Keyword(s):  
Land Use ◽  
Tibetan Plateau ◽  
Land Cover ◽  
Change Detection ◽  
Dynamic Change ◽  
Coniferous Forest ◽  
Three Dimensional ◽  
Detection Methods ◽  
Land Cover Changes ◽  
The Tibetan Plateau

AbstractRegarded as the third pole of the Earth, the Tibetan Plateau (TP) is a region with complex terrain. Vegetation is widely distributed in the southeastern part of the plateau. However, the land use and land cover changes (LULCC) on the TP have not been sufficiently studied. In this study, we propose a method of studying the dynamic changes in the land cover on the TP. Landsat OLI images (2013 and 2015) were selected to extract the LULCC information of Nyingchi County, the DEM was used to extract objects’ land curved surface area and analyze their three-dimensional dynamic change information, which realized a four-dimensional monitoring of the forestry information on time and spatial level. The results showed that the forest area in 2015 decreased by 7.25%, of which the coniferous forest areas decreased by 25.14%, broad-leaved forest areas increased by 12.65%, and shrubbery areas increased by 14.62%. Compared with traditional LULCC detection methods, the change detection is no longer focused on the two-dimensional space, which helps determine the three-dimensional land use and land cover changes and their distribution. Thus, dynamic spatial changes can be observed. This study provides scientific support for the vegetation restoration and natural resource management on the TP.

scholarly journals HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images

2021 ◽  
Vol 13 (8) ◽  
pp. 1440
Author(s):  
Yi Zhang ◽  
Lei Fu ◽  
Ying Li ◽  
Yanning Zhang
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Aerial Images ◽  
Aerial Image ◽  
Sufficient Information ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Deep Learning Features ◽  
Global And Local

Accurate change detection in optical aerial images by using deep learning techniques has been attracting lots of research efforts in recent years. Correct change-detection results usually involve both global and local deep learning features. Existing deep learning approaches have achieved good performance on this task. However, under the scenarios of containing multiscale change areas within a bi-temporal image pair, existing methods still have shortcomings in adapting these change areas, such as false detection and limited completeness in detected areas. To deal with these problems, we design a hierarchical dynamic fusion network (HDFNet) to implement the optical aerial image-change detection task. Specifically, we propose a change-detection framework with hierarchical fusion strategy to provide sufficient information encouraging for change detection and introduce dynamic convolution modules to self-adaptively learn from this information. Also, we use a multilevel supervision strategy with multiscale loss functions to supervise the training process. Comprehensive experiments are conducted on two benchmark datasets, LEBEDEV and LEVIR-CD, to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.

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