Automatic Digital Aerial Image Resection Controlled by LIDAR Data

2009 ◽  
pp. 213-234
Author(s):  
Roosevelt De Lara ◽  
Edson A. Mitishita ◽  
Thomas Vögtle ◽  
Hans-Peter Bähr
Keyword(s):  
Aerial Image ◽  
Lidar Data

A multi-scale registration of urban aerial image with airborne lidar data

2015 ◽  
Author(s):  
Shuo Huang ◽  
Siying Chen ◽  
Yinchao Zhang ◽  
Pan Guo ◽  
He Chen
Keyword(s):  
Airborne Lidar ◽  
Aerial Image ◽  
Lidar Data ◽  
Multi Scale ◽  
Airborne Lidar Data

scholarly journals A Novel Method for Feature Extraction and Classification for an Aerial Image and LiDAR Data with Genetic Algorithm

2012 ◽  
pp. 264-270
Author(s):  
Manjunath B. E ◽  
D. G. Anand ◽  
Mahant. G. Kattimani
Keyword(s):  
Feature Extraction ◽  
Light Detection And Ranging ◽  
Aerial Image ◽  
Lidar Data ◽  
Light Detection ◽  
Aerial Photos ◽  
The Earth ◽  
Digital Elevation ◽  
Land Surveying ◽  
Novel Method

Airborne Light Detection and Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. In particular, LiDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. Aerial photos with LiDAR data were processed with genetic algorithms not only for feature extraction but also for orthographical image. DSM provided by LiDAR reduced the amount of GCPs needed for the regular processing, thus the reason both efficiency and accuracy are highly improved. LiDAR is an acronym for Light Detection and Ranging, which is typically defined as an integration of three technologies into a single system, which is capable of acquiring a data to produce accurate Digital Elevation Models.

scholarly journals FAST AND ROBUST REGISTRATION OF AERIAL IMAGES AND LIDAR DATA BASED ON STRUCTURAL FEATURES AND 3D PHASE CORRELATION

2020 ◽  
Vol V-2-2020 ◽  
pp. 135-141
Author(s):  
B. Zhu ◽  
Y. Ye ◽  
C. Yang ◽  
L. Zhou ◽  
H. Liu ◽  
...  
Keyword(s):  
Structural Features ◽  
Phase Correlation ◽  
Aerial Images ◽  
Aerial Image ◽  
Reference Image ◽  
Feature Descriptor ◽  
Lidar Data ◽  
Three Dimension ◽  
Interest Points ◽  
Registration Method

Abstract. Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial image by a partition strategy and perform a local geometric correction by using the collinearity equation to eliminate scale and rotation difference between images. Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT). Finally, the obtained CPs are employed to correct the exterior orientation elements, which is used to achieve co-registration of aerial images and LiDAR data. Experiments with two datasets of aerial images and LiDAR data show that the proposed method is much faster and more robust than state of the art methods.

The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

2021 ◽  
Author(s):  
Ulaş Yunus ÖZKAN ◽  
Tufan Demirel
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Window Size ◽  
Aerial Image ◽  
Lidar Data ◽  
Black Pine ◽  
Sensing Data ◽  
Structural Variables ◽  
Pine Stands ◽  
Optimal Window

Abstract Background: Determining the appropriate window size is a critical step for estimating stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.Results: In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size is optimal for N and H, the 600 m2 window size is optimal for V and the 1000 m2 window size is optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion: This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

scholarly journals COMPARISON OF 3D POINT CLOUDS FROM AERIAL STEREO IMAGES AND LIDAR FOR FOREST CHANGE DETECTION

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 1-5 ◽  
Author(s):  
D. Ali-Sisto ◽  
P. Packalen
Keyword(s):  
Classification Accuracy ◽  
Digital Terrain Model ◽  
Ground Level ◽  
Point Clouds ◽  
Aerial Image ◽  
Lidar Data ◽  
Forest Change ◽  
Time Points ◽  
3D Point Clouds ◽  
The Difference

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

scholarly journals OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE

2016 ◽  
Vol III-7 ◽  
pp. 45-50 ◽  
Author(s):  
R. T. Alberto ◽  
S. C. Serrano ◽  
G. B. Damian ◽  
E. E. Camaso ◽  
A. B. Celestino ◽  
...  
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
Texture Classification ◽  
Land Cover Classification ◽  
Aerial Image ◽  
Support Vector ◽  
Lidar Data ◽  
Segmentation Approach ◽  
Great Possibility ◽  
Area Classification

Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.

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