scholarly journals Novel and robust methods for the automatic registration of image data

2020 ◽  
Author(s):  
Κωνσταντίνος Σπανάκης
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithms ◽  
Surrogate Model ◽  
Harmony Search ◽  
Image Data ◽  
Image Stitching ◽  
Automatic Registration ◽  
Robust Methods ◽  
Renyi’S Divergence

Η Ταύτιση εικόνων είναι η διαδικασία του γεωμετρικού μετασχηματισμού δύο ή περισσότερων εικόνων με σκοπό τα κοινά τους σημεία να έχουν την ίδια θέση στο χώρο, κι έχει πολλές εφαρμογές όπως στην Ιατρική απεικόνιση, Remote Sensing και Συρραφή Εικόνων (Image Stitching). Παρά την επιστημονική πρόοδο που έχει επιτευχθεί τα τελευταία 40 χρόνια, εξακολουθούν να υπάρχουν άλυτα θέματα που σχετίζονται με Ακρίβεια, Υπολογιστικό Κόστος, Σύγκλιση σε τοπικά μέγιστα και διαδικασίες Αυτοματισμού των αριθμητικών μεθόδων ταύτισης εικόνων. Αυτά με την σειρά τους επηρεάζονται από το Μέτρο Ομοιότητας των εικόνων, τον Γεωμετρικό Μετασχηματισμό και την Μέθοδο βελτιστοποίησης που χρησιμοποιούνται. Οι μαθηματικές/στατιστικές μέθοδοι για την σύγκριση εικόνων έχουν αποδειχθεί πολύ αποτελεσματικότερες των μεθόδων που χρησιμοποιούν χαρακτηριστικά των εικόνων όπως τα σημεία τους. Επιπλέον, απαιτούν ελάχιστη (αν όχι καθόλου) προεπεξεργασία των εικόνων, πράγμα που τις καθιστά αυτόματες. Επειδή χρησιμοποιούν ένα σημαντικό τμήμα των εικόνων για την εκτίμηση της ομοιότητας, καθίστανται υπολογιστικά πολύ ακριβές ιδιαίτερα όταν χρειαστεί να γίνει εκτενής αναζήτηση του βέλτιστου μετασχηματισμού. Στα πλαίσια της διατριβής αυτής, έγινε εκτενής έρευνα σχετικά με τις Μεθόδους Βελτιστοποίησης και τα Μέτρα Σύγκρισης των εικόνων. Συγκεκριμένα, έγινε έρευνα σε Ελιτιστικούς Γενετικούς Αλγορίθμους (Elitist Genetic Algorithms) καθώς και σε νέες παραλλαγές μίας άλλης μεθόδου βελτιστοποίησης γνωστή ως Αρμονική Αναζήτηση (Harmony Search). Επίσης, κατασκευάστηκε και μία μέθοδος, με σκοπό την μείωση του υπολογιστικού κόστους, γνωστή ως Surrogate Model. Τέλος, στα πλαίσια εκτίμησης ομοιότητας των εικόνων, έγινε σύγκριση στατιστικών μέτρων βασισμένα στην στατιστική απόκλιση του Renyi (Renyi’s Divergence) με σκοπό την χρήση όσο είναι δυνατόν μικρότερου ποσοστού των εικόνων χωρίς την ταυτόχρονη μείωση της ποιότητας των αποτελεσμάτων.

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

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.

scholarly journals A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 747
Author(s):  
Yanghua Di ◽  
Zhiguo Jiang ◽  
Haopeng Zhang
Keyword(s):  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Visual Categorization ◽  
Class Differences ◽  
Fine Grained ◽  
Ship Classification

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

scholarly journals Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021 ◽  
Vol 13 (3) ◽  
pp. 531
Author(s):  
Caiwang Zheng ◽  
Amr Abd-Elrahman ◽  
Vance Whitaker
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Precision Agriculture ◽  
Image Data ◽  
Fruit Shape ◽  
Biological Information ◽  
Genetic Traits ◽  
Multiple Sensors ◽  
Fruit Maturity ◽  
High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

Unsupervised labelling of remote sensing images based on force field clustering

2021 ◽  
pp. 1-14
Author(s):  
Zhenggang Wang ◽  
Jin Jin
Keyword(s):  
Remote Sensing ◽  
Force Field ◽  
Image Data ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Initial Cluster ◽  
Data Points ◽  
Global Optimal ◽  
Density Force

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and Inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.

Feature Extraction in Remote Sensing High-Dimensional Image Data

2007 ◽  
Vol 4 (1) ◽  
pp. 107-111 ◽  
Author(s):  
Maciel Zortea ◽  
Victor Haertel ◽  
Robin Clarke
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Data ◽  
High Dimensional ◽  
Dimensional Image

scholarly journals Summaries of certain spatial patterns retrieved from multidate remote-sensing data

2004 ◽  
Vol 2004 (2) ◽  
pp. 287-300
Author(s):  
Hema Nair
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Spatial Patterns ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Geographic Area ◽  
Image Mining ◽  
Sensing Data

This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as “Y isF” can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.

Sign in / Sign up

Export Citation Format

Share Document