scholarly journals AUTOMATIC ROAD EXTRACTION BASED ON INTEGRATION OF HIGH RESOLUTION LIDAR AND AERIAL IMAGERY

2015 ◽  
Vol XL-1-W5 ◽  
pp. 583-587 ◽  
Author(s):  
S. Rahimi ◽  
H. Arefi ◽  
R. Bahmanyar
Keyword(s):  
High Resolution ◽  
Principal Component ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Road Extraction ◽  
The Road ◽  
Human Effort ◽  
Fully Automatic ◽  
Straight Lines ◽  
Road Network Detection

In recent years, the rapid increase in the demand for road information together with the availability of large volumes of high resolution Earth Observation (EO) images, have drawn remarkable interest to the use of EO images for road extraction. Among the proposed methods, the unsupervised fully-automatic ones are more efficient since they do not require human effort. Considering the proposed methods, the focus is usually to improve the road network detection, while the roads’ precise delineation has been less attended to. In this paper, we propose a new unsupervised fully-automatic road extraction method, based on the integration of the high resolution LiDAR and aerial images of a scene using Principal Component Analysis (PCA). This method discriminates the existing roads in a scene; and then precisely delineates them. Hough transform is then applied to the integrated information to extract straight lines; which are further used to segment the scene and discriminate the existing roads. The roads’ edges are then precisely localized using a projection-based technique, and the round corners are further refined. Experimental results demonstrate that our proposed method extracts and delineates the roads with a high accuracy.

scholarly journals A Method for Road Extraction from High-Resolution Remote Sensing Images Based on Multi-Kernel Learning

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 385
Author(s):  
Rui Xu ◽  
Yanfang Zeng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Kernel Learning ◽  
Morphological Operations ◽  
Road Extraction ◽  
The Road ◽  
Topological Features ◽  
Important Research Topic ◽  
Wide Range ◽  
Quantitative Results

Extracting road from high resolution remote sensing (HRRS) images is an economic and effective way to acquire road information, which has become an important research topic and has a wide range of applications. In this paper, we present a novel method for road extraction from HRRS images. Multi-kernel learning is first utilized to integrate the spectral, texture, and linear features of images to classify the images into road and non-road groups. A precise extraction method for road elements is then designed by building road shaped indexes to automatically filter out the interference of non-road noises. A series of morphological operations are also carried out to smooth and repair the structure and shape of the road element. Finally, based on the prior knowledge and topological features of the road, a set of penalty factors and a penalty function are constructed to connect road elements to form a complete road network. Experiments are carried out with different sensors, different resolutions, and different scenes to verify the theoretical analysis. Quantitative results prove that the proposed method can optimize the weights of different features, eliminate non-road noises, effectively group road elements, and greatly improve the accuracy of road recognition.

Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline

2021 ◽  
Author(s):  
Mirela Beloiu ◽  
Dimitris Poursanidis ◽  
Samuel Hoffmann ◽  
Nektarios Chrysoulakis ◽  
Carl Beierkuhnlein
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Temporal Dynamics ◽  
Tree Density ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Remote Areas ◽  
Spatio Temporal ◽  
Study Sites

<p>Recent advances in deep learning techniques for object detection and the availability of high-resolution images facilitate the analysis of both temporal and spatial vegetation patterns in remote areas. High-resolution satellite imagery has been used successfully to detect trees in small areas with homogeneous rather than heterogeneous forests, in which single tree species have a strong contrast compared to their neighbors and landscape. However, no research to date has detected trees at the treeline in the remote and complex heterogeneous landscape of Greece using deep learning methods. We integrated high-resolution aerial images, climate data, and topographical characteristics to study the treeline dynamic over 70 years in the Samaria National Park on the Mediterranean island of Crete, Greece. We combined mapping techniques with deep learning approaches to detect and analyze spatio-temporal dynamics in treeline position and tree density. We use visual image interpretation to detect single trees on high-resolution aerial imagery from 1945, 2008, and 2015. Using the RGB aerial images from 2008 and 2015 we test a Convolution Neural Networks (CNN)-object detection approach (SSD) and a CNN-based segmentation technique (U-Net). Based on the mapping and deep learning approach, we have not detected a shift in treeline elevation over the last 70 years, despite warming, although tree density has increased. However, we show that CNN approach accurately detects and maps tree position and density at the treeline. We also reveal that the treeline elevation on Crete varies with topography. Treeline elevation decreases from the southern to the northern study sites. We explain these differences between study sites by the long-term interaction between topographical characteristics and meteorological factors. The study highlights the feasibility of using deep learning and high-resolution imagery as a promising technique for monitoring forests in remote areas.</p>

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

scholarly journals High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3774 ◽  
Author(s):  
Xuran Pan ◽  
Lianru Gao ◽  
Bing Zhang ◽  
Fan Yang ◽  
Wenzhi Liao
Keyword(s):  
High Resolution ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Sensor Data ◽  
Median Frequency ◽  
Feature Maps ◽  
Class Imbalance Problem ◽  
Semantic Labeling

Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambiguities. Based on high-level contextual features, the deep convolutional neural network (DCNN) is an effective method to deal with semantic segmentation of high-resolution aerial imagery. In this work, a novel dense pyramid network (DPN) is proposed for semantic segmentation. The network starts with group convolutions to deal with multi-sensor data in channel wise to extract feature maps of each channel separately; by doing so, more information from each channel can be preserved. This process is followed by the channel shuffle operation to enhance the representation ability of the network. Then, four densely connected convolutional blocks are utilized to both extract and take full advantage of features. The pyramid pooling module combined with two convolutional layers are set to fuse multi-resolution and multi-sensor features through an effective global scenery prior manner, producing the probability graph for each class. Moreover, the median frequency balanced focal loss is proposed to replace the standard cross entropy loss in the training phase to deal with the class imbalance problem. We evaluate the dense pyramid network on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2D semantic labeling dataset, and the results demonstrate that the proposed framework exhibits better performances, compared to the state of the art baseline.

scholarly journals Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity

2018 ◽  
Vol 10 (8) ◽  
pp. 1284 ◽  
Author(s):  
Zhiqiang Zhang ◽  
Xinchang Zhang ◽  
Ying Sun ◽  
Pengcheng Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Road Networks ◽  
Aerial Images ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Remote Sensing Images ◽  
The Road ◽  
Image Objects ◽  
Very High

The road networks provide key information for a broad range of applications such as urban planning, urban management, and navigation. The fast-developing technology of remote sensing that acquires high-resolution observational data of the land surface offers opportunities for automatic extraction of road networks. However, the road networks extracted from remote sensing images are likely affected by shadows and trees, making the road map irregular and inaccurate. This research aims to improve the extraction of road centerlines using both very-high-resolution (VHR) aerial images and light detection and ranging (LiDAR) by accounting for road connectivity. The proposed method first applies the fractal net evolution approach (FNEA) to segment remote sensing images into image objects and then classifies image objects using the machine learning classifier, random forest. A post-processing approach based on the minimum area bounding rectangle (MABR) is proposed and a structure feature index is adopted to obtain the complete road networks. Finally, a multistep approach, that is, morphology thinning, Harris corner detection, and least square fitting (MHL) approach, is designed to accurately extract the road centerlines from the complex road networks. The proposed method is applied to three datasets, including the New York dataset obtained from the object identification dataset, the Vaihingen dataset obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D semantic labelling benchmark and Guangzhou dataset. Compared with two state-of-the-art methods, the proposed method can obtain the highest completeness, correctness, and quality for the three datasets. The experiment results show that the proposed method is an efficient solution for extracting road centerlines in complex scenes from VHR aerial images and light detection and ranging (LiDAR) data.

scholarly journals AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION

2020 ◽  
Vol V-2-2020 ◽  
pp. 925-932
Author(s):  
Y. Wei ◽  
X. Hu ◽  
M. Zhang ◽  
Y. Xu
Keyword(s):  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Tensor Voting ◽  
Road Extraction ◽  
The Road ◽  
Road Width ◽  
Road Intersections ◽  
On The Road ◽  
Segmentation Problem

Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.

scholarly journals Classification of Soybean Pubescence from Multispectral Aerial Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Robert W. Bruce ◽  
Istvan Rajcan ◽  
John Sulik
Keyword(s):  
Plant Breeding ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accurate Determination ◽  
Principal Component ◽  
Training Data ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Support Vector

The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescence classes, and test a machine learning (ML) classification approach. A principal component analysis (PCA) on hyperspectral soybean plot data identified clusters related to pubescence classes, while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability. Aerial images from 2018, 2019, and 2020 were analyzed in this study. A 60-plot test (2019) of genotypes with known pubescence was used as reference data, while whole-field images from 2018, 2019, and 2020 were used to examine the broad applicability of the classification methodology. Two indices, a red/blue ratio and blue normalized difference vegetation index (blue NDVI), were effective at differentiating tawny and gray pubescence types in high-resolution imagery. A ML approach using a support vector machine (SVM) radial basis function (RBF) classifier was able to differentiate the gray and tawny types (83.1% accuracy and kappa=0.740 on a pixel basis) on images where reference training data was present. The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel, indicating limitations of using aerial imagery for pubescence classification in some environmental conditions. High-throughput classification of gray and tawny pubescence types is possible using aerial imagery, but light tawny soybeans remain difficult to classify and may require training data from each field season.

scholarly journals Extração de Rodovias Utilizando Snakes

2004 ◽  
Vol 31 (2) ◽  
pp. 31
Author(s):  
RAFAEL MONTANHINI SOARES DE OLIVEIRA ◽  
ALUIR PORFIRO DAL POZ
Keyword(s):  
Initial Position ◽  
Geometric Constraints ◽  
Aerial Images ◽  
Road Extraction ◽  
Parametric Curve ◽  
The Road ◽  
Internal Forces ◽  
Contour Models ◽  
External Forces ◽  
Seed Points

This paper presents a methodology for semi-automatic road extraction from digital images using active contour models or snakes. Snakes was proposed almost two decades ago, consisting in a parametric curve controlled by photometric and geometric constraints. This last ones generate internal forces that control the shape of the snakes curve. The photometric constraints generate external forces that attract the snakes curve to the selected feature, i.e., the road. As this snakes-based method is semi-automatic, firstly the operator must describe the road roughly using at least six seed points. From this initial position, the snakes curve moves around at each interation, stabilizing when it coincides with the road. This road extraction methodology was tested with digital aerial images of different resolution. The experimental results obtained with these images showed the method’s potential in accurate and reliable road extraction.

scholarly journals RESEARCH ON BROKEN ROAD CONNECTION METHOD AFTER ROAD EXTRACTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 387-395
Author(s):  
D. L. Fan ◽  
B. Wang ◽  
Z. L. Chen ◽  
L. Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Power Grids ◽  
Road Extraction ◽  
The Road ◽  
Isolated Points

Abstract. Aiming at the problem of disconnection after road classification of remote sensing image, this paper proposes an optimization method for broken road connection considering spatial connectivity. The method extracts the road skeleton based on the binarized image after road extraction, and uses the eight neighborhood detection algorithm to find the road breakpoints after road extraction of high-resolution remote sensing image, and removes the isolated points of the road edge according to mathematical morphology filtering. Secondly, use K-means clustering algorithm to search for road breakpoints, and eliminate invalid breakpoints; then, fit the breakpoints of each category through polynomial curves, and record the mathematics of each fitted curve expression; Finally, the coordinate sequences between each kind of breakpoint is calculated according to each fitted polynomial, and the corresponding pixel is filled with the width of the road to realize automatic detection and connection. In this paper, the images after road extraction based on the U-Net network is used to test the method. The results show that the proposed method can better connect the roads formed by road or building shadows. Especially, the single broken road , has a high integrity of the road shape after repairing. The method proposed in this paper has certain reference significance for the classification and repair of linear objects such as roads, power grids and tracks.

scholarly journals A ROAD EXTRACTION METHOD BASED ON HIGH RESOLUTION REMOTE SENSING IMAGE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 671-676 ◽  
Author(s):  
X. Zhang ◽  
C. K. Zhang ◽  
H. M. Li ◽  
Z. Luo
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Extraction Method ◽  
Mean Shift ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Mean Shift Algorithm ◽  
The Road ◽  
Stroke Width ◽  
Transformation Algorithm

Abstract. Aiming at the road extraction in high-resolution remote sensing images, the stroke width transformation algorithm is greatly affected by surrounding objects, and it is impossible to directly obtain high-precision road information. A new road extraction method combining stroke width transformation and mean drift is proposed. In order to reduce road holes and discontinuities, and preserve better edge information, the algorithm first performs denoising preprocessing by means of median filtering to the pre-processed image. Then, the mean shift algorithm is used for image segmentation. The adjacent parts of the image with similar texture and spectrum are treated as the same class, and then the fine areas less than the maximum stroke width are reduced. On the basis , the road information is extracted by the stroke width transformation algorithm, and the information also contains a small amount of interference information such as spots (non-road). In order to further improve road extraction accuracy and reduce speckle and non-road area interference, the basic operations and combinations in mathematical morphology are used to optimize it. The experimental results show that the proposed algorithm can accurately extract the roads on high-resolution remote sensing images, and the better the road features, the better the extraction effect. However, the applicability of the algorithm is greatly affected by the surrounding objects.

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