Image Data Analysis in Remote Sensing

Abstract In the continuous innovation of modern technology concept, remote sensing technology as an advanced and practical comprehensive detection technology has been widely used in many fields. Especially for environmental monitoring, the rational use of remote sensing image data analysis and processing platform can not only obtain valuable environmental information, but also provide effective management decisions for climate changeable natural disasters and other issues. Therefore, on the basis of understanding the design scheme of remote sensing image data analysis and processing platform system, this paper makes clear the positive role of remote sensing image processing technology in the development of environmental monitoring based on the application of the platform.

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Remote sensing measurement of forest parameters in the insular part of South-East Asia : high/low resolution image data analysis

Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1993.322232 ◽

2002 ◽

Author(s):

C. Estreguil ◽

G. D'Souza

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

East Asia ◽

Image Data ◽

South East Asia ◽

Low Resolution ◽

Resolution Image

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Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

Complexity ◽

10.1155/2021/9933330 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Chenhua Zu

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Data Storage ◽

Image Data ◽

Remote Sensing Image ◽

Development Environment ◽

Log Data ◽

Storage And Retrieval ◽

Web Text Mining ◽

Log Data Analysis

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.

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Noise estimation for remote sensing image data analysis

10.1117/12.508101 ◽

2004 ◽

Cited By ~ 1

Author(s):

Qian Du

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Image Data ◽

Remote Sensing Image ◽

Noise Estimation

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IMPROVING GEOGRAPHY PRE-SERVICE TEACHERS’ UNDERSTANDING OF SATELLITE IMAGERY ANALYSIS USING ER MAPPER SOFTWARE WITH A MODULE

International Journal of Education ◽

10.17509/ije.v11i1.11162 ◽

2018 ◽

Vol 11 (1) ◽

pp. 59

Author(s):

Dede Sugandi ◽

Lili Somantri

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Data Analysis ◽

Satellite Imagery ◽

Learning Process ◽

Image Data ◽

Learning Objectives ◽

Average Speed ◽

Class Average ◽

The Difference

The development of student potential is influenced by how the teacher prepares the steps of the learning process. This means that teacher’s preparation and planning for the learning process determines the achievement of learning objectives. There is an indication that students have low mastery in the skills of imagery analysis on the topic of remote sensing. This research aims to find ways to assist students in the learning process of remote sensing and find about the appropriateness of the form of learning process to the learning materials as well as the allocated time and the steps in the learning process. More specifically, it aims to assist students in gaining understanding and ability of image data analysis using Er Mapper software. The population consisted of 195 students, while the sample taught to improve their understanding of the steps taken in imagery analysis only included 90 students. The students were divided into two groups, the control class and the experimental class, with 45 students respectively. In assessing students' understanding, this research was aided by four testers who assessed and determined the time taken for students to complete each stage of imagery analysis, with the criteria of: 1) 0-1 (very fast), 2)> 1-2 (fast), 3)> 2-3 (slow), and 4)> 3-4 (very slow). The scores of the imagery analysis of the control class and experimental class students were 3.30 (very good) and 3.38 (very good), respectively, with a difference of 0.08. The difference in the understanding and ability was more strongly indicated by the speed or time taken to complete the analysis stages for each meeting. The control class’ average speed was 3.51 (very slow), while the experimental class was 1.44 (fast). To accelerate the learning process of image analysis, the lab work should be assisted with guidebooks or modules in each meeting. With the module, students can learn independently.

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Image data analysis and classification in marketing

Advances in Data Analysis and Classification ◽

10.1007/s11634-012-0116-0 ◽

2012 ◽

Vol 6 (4) ◽

pp. 253-276 ◽

Cited By ~ 8

Author(s):

Daniel Baier ◽

Ines Daniel ◽

Sarah Frost ◽

Robert Naundorf

Keyword(s):

Data Analysis ◽

Image Data

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Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

Sustainability ◽

10.3390/su13041917 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1917

Author(s):

Alma Elizabeth Thuestad ◽

Ole Risbøl ◽

Jan Ingolf Kleppe ◽

Stine Barlindhaug ◽

Elin Rose Myrvoll

Keyword(s):

Remote Sensing ◽

Laser Scanning ◽

Digital Terrain Model ◽

Image Data ◽

Remote Sensing Image ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Detection Rates ◽

Airborne Laser ◽

Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

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Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

Mathematics ◽

10.3390/math9111305 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1305

Author(s):

Feliu Serra-Burriel ◽

Pedro Delicado ◽

Fernando M. Cucchietti

Keyword(s):

Remote Sensing ◽

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Vegetation Index ◽

Vegetation Recovery ◽

Functional Responses ◽

Elapsed Time ◽

Synthetic Controls ◽

Available Information

In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.

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