scholarly journals A priori accuracy assessment methodology of the geometric alignment of multi-temporal multispectral images of the Earth’s surface

2020 ◽  
Vol 17 (7) ◽  
pp. 19-25
Author(s):  
A.E. Kuznetsov ◽  
◽  
P.N. Svetelkin ◽  
V.I. Poshekhonov ◽  
◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
A Priori ◽  
Multispectral Images ◽  
Assessment Methodology ◽  
Multi Temporal

scholarly journals Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving

2021 ◽  
Vol 10 (11) ◽  
pp. 761
Author(s):  
Tengfei Yu ◽  
He Huang ◽  
Nana Jiang ◽  
Tri Dev Acharya
Keyword(s):  
Accuracy Assessment ◽  
A Priori ◽  
Autonomous Driving ◽  
Relative Accuracy ◽  
Accuracy Evaluation ◽  
High Definition ◽  
Verification Method ◽  
Road Signs ◽  
Point Set ◽  
Priori Information

High-definition maps (HDM) for autonomous driving (AD) are an important component of AD systems. HDMs accurately provide a priori information, including lane lines, and road signs, for AD systems. It is an important task to make a reasonable accuracy assessment of the HDM. The current methods for relative accuracy evaluation of general maps in the field of mapping are not fully applicable to HDMs. In this study, a method based on point set alignment and resampling is used to evaluate the relative accuracy of lane lines, and experiments are conducted based on relevant real HDM data. The results show that the relative accuracy of the lane lines is more detailed and relevant than the traditional method. This has implications for the quality control of HDM production.

Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area

2015 ◽  
Vol 6 (4) ◽  
pp. 83-102 ◽  
Author(s):  
Eufemia Tarantino ◽  
Antonio Novelli ◽  
Mariella Aquilino ◽  
Benedetto Figorito ◽  
Umberto Fratino
Keyword(s):  
Land Cover ◽  
A Priori ◽  
Sensor Data ◽  
Network Simulator ◽  
Research Activity ◽  
The Past ◽  
Time Space ◽  
Multi Temporal ◽  
Study Case ◽  
Landsat Satellite

This paper analyzes two pixel-based classification approaches to support the analysis of land cover transformations based on multitemporal LANDSAT sensor data covering a time space of about 24 years. The research activity presented in this paper was carried out using Lama San Giorgio (Bari, Italy) catchment area as a study case, being this area prone to flooding as proved by its geological and hydrological characteristics and by the significant number of floods occurred in the past. Land cover classes were defined in accordance with on the CN method with the aim of characterizing land use based on attitude to generate runoff. Two different classifiers, i.e. Maximum Likelihood Classifier (MLC) and Java Neural Network Simulator (JavaNNS) models, were compared. The Artificial Neural Networks (ANN) approach was found to be the most reliable and efficient when lacking ground reference data and a priori knowledge on input data distribution.

scholarly journals Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1907 ◽  
Author(s):  
Luís Pádua ◽  
Pedro Marques ◽  
Jonáš Hruška ◽  
Telmo Adão ◽  
Emanuel Peres ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Low Cost ◽  
Ground Truth ◽  
Volume Estimation ◽  
Height Range ◽  
Ground Truth Data ◽  
Operational Technique ◽  
Digital Elevation ◽  
Multi Temporal ◽  
Aerial Vehicle

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

scholarly journals Change detection analysis of reservoirs and lakes in Multi-Temporal Landsat-7 (ETM+) data over the Indian sub-continent during 2008-2018

2018 ◽  
Vol 7 (4.6) ◽  
pp. 122
Author(s):  
B. Chandrababu Naik ◽  
Prof. B. Anuradha ◽  
. .
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Kappa Statistics ◽  
Detection Techniques ◽  
Change Detection Analysis ◽  
Detection Analysis ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Lakes And Reservoirs

Remote sensing change detection techniques are extensively used in numerous applications such as land cover monitoring, disaster monitoring, and urban sprawl. The main motive of this paper study the change detection analysis of Land Use / Land Cover (LULC) in different lakes and Reservoirs, such as Chilika Lake, Pulicat Lake, Vembanad Lake, Penna Reservoir, and Nagarjuna Sagar Reservoir located in the Indian subcontinent region.  The analyses and changes are evaluated during period of 2008 - 2018 in multi-temporal Landsat-7 (ETM+) data. The major disadvantage in Landsat-7 for data acquired from satellite sensor, is that it includes strips (gaps) in an image. On May 31, 2003 the Scan-Line-Corrector (SLC) failed completely, due to 22% of pixel information lost in the Landsat-7 data. The focal analysis method is applied to the required image for removing all strips (gaps). Change detection using Image Differencing technique, maximum changed area and unchanged area detect the different Lakes and Reservoirs in the period of 2008-2018. The unsupervised classification is used to compute the accuracy assessment analysis. Excellent results are obtained by using accuracy assessment for different Lakes and Reservoirs from 2008 to 2018, with the overall accuracy of 91.59%, and overall kappa statistics of 0.9032. The percentage of a decreased area is more in 2018 as compared to 2008 and it concludes that the percentage of decreased area is more as compared to the percentage of increased area for acquired Landsat-7 data.  

scholarly journals AREA ESTIMATION OF MULTI-TEMPORAL GLOBAL IMPERVIOUS LAND COVER BASED ON STRATIFIED RANDOM SAMPLING

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 103-108
Author(s):  
Y. Gong ◽  
H. Xie ◽  
X. Tong ◽  
Y. Jin ◽  
X. Xv ◽  
...  
Keyword(s):  
Land Cover ◽  
Random Sampling ◽  
Accuracy Assessment ◽  
Time Interval ◽  
Stratified Random Sampling ◽  
Regional Environment ◽  
Area Estimation ◽  
Research On Evaluation ◽  
Multi Temporal ◽  
Land Cover Maps

Abstract. Estimating area of impervious land cover is the most useful and one of the ecological assessment indexes of urban and regional environment. Global land cover maps are inevitably misclassified, which affects the quality and application of the data. Statistical approach for assessing the accuracy is critical to understand the global change information and area estimation is usually based on sample data with a probability-based estimator. However, research on evaluation of multi-temporal global impervious land cover maps has not been implemented. In this study, spatial characteristics of the data are considered to assess the thematic map accuracy with a two-stage stratified random sampling plan. The first stage of stratification is determined by the global urban ecoregion and the second one is determined by land cover classes. Additionally, sample size of both map stage and pixel stage are calculated using a probability sampling model. A response design is constructed for a per-pixel accuracy assessment and blind interpretation is implemented using sample pixels and its surrounding area. Our method is applied to the multi-temporal global impervious land cover maps between 2000 and 2010 with a time interval of 5 years and the estimated area in different epoch are listed in detail. The main contribution of our research is illustrating the details for calculating the proportion area of impervious land cover and corresponding confidence intervals based on the reference classification. The experimental results show that the increasing area of the impervious surface according to the sample unit shows good agreement with the urbanization and descriptive accuracy assessments by user’s, producer’s and overall accuracy are shown respectively.

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