scholarly journals SEMI-AUTOMATED DELINEATION OF INFORMAL SETTLEMENT STRUCTURES FROM DRONE RGB IMAGERY USING OBJECT-BASED IMAGE ANALYSIS

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 249-256
Author(s):  
R. A. B. Rivera ◽  
E. N. B. Idago ◽  
A. C. Blanco ◽  
K. A. P. Vergara
Keyword(s):  
Image Analysis ◽  
Online Survey ◽  
Informal Settlement ◽  
The Philippines ◽  
Object Based Image Analysis ◽  
Survey Form ◽  
Object Based ◽  
Quality Checks ◽  
Rule Set ◽  
Elevation Model

Abstract. With the problem of informal settlements in the Philippines, mapping such areas is the first step towards improvement. Object-based image analysis (OBIA) has been a powerful tool for mapping and feature extraction, especially for high-resolution datasets. In this study, an informal settlement area in UP Diliman, Quezon City was chosen to be the subject site, where individual informal settlement structures (ISS) were delineated and estimated using OBIA. With the help of photogrammetry and image enhancement techniques, derivatives such as elevation model and orthophotos were produced for easier interpretation. An initial rule-set was developed to remove all non-ISS features from the base image–utilizing spectral values and thematic layers as main classifiers. This classification technique yielded a 94% accuracy for non-ISS class, and 92% for the possible ISS class. Another rule-set was then developed to delineate individual ISS based on the texture and elevation model of the area, which paved the way for the estimation of ISS count. To test the robustness of the methodology developed, the estimation results were compared to the manual count obtained through an online survey form, and the classification and delineation results were assessed through overall and individual quality checks. The estimation yielded a relative accuracy of 60%, which came from the delineation rate of 63%. On the other hand, delineation accuracy was calculated through area-based and number-based measures, yielding 58% and 95%, respectively. Issues such as noisy elevation models and physical limitations of the area and survey done affected the accuracy of the results.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

scholarly journals AN OBJECT BASED IMAGE ANALYSIS APPROACH FOR THE EXTRACTION OF THE KOLOUMBO VOLCANO AND ASSOCIATED DOMES-CONES FROM A DIGITAL SEABED ELEVATION MODEL

2017 ◽  
Vol 50 (3) ◽  
pp. 1616
Author(s):  
D. Βampourda ◽  
D. Argialas ◽  
P. Nomikou ◽  
A. Tzotsos
Keyword(s):  
Image Analysis ◽  
Analysis Approach ◽  
Topographic Position Index ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Digital Elevation ◽  
Position Index ◽  
Elevation Model ◽  
Comparison Of The Results ◽  
Geomorphological Features

This paper concerns the study of the seafloor through digital seabed elevation models, using object based image analysis methods. The goal of this research was the automated extraction of geomorphological features from the seabed in regions presenting intense volcanic activity. The study area is located around the submarine volcano of the Kolοumbo (in the submarine area northeast of the Santorini island, Greece). For this purpose, a Digital Elevation Model (DEM) of the seabed with a spatial resolution of 50m was used. Derivatives of the DEM, such us Slope, Topographic Position Index (TPI) and Terrain Ruggedness Index (TRI) were created in the open source software "QGIS 2.4". The implementation of the object based image analysis approach was performed in eCognition 8.7 software. Nine segmentation and classification levels were created in order to produce the final level segmentation "level 5", where the final geomorphological features were classified. The results of the method were evaluated using classification stability measures and qualitative and quantitative comparison of the results with existing map.

scholarly journals AUTOMATIC IDENTIFICATION OF SUBMARINE LANDFORMS USING OBJECT BASED IMAGE ANALYSIS IN THE AREA OF NORTH AEGEAN BASIN

2017 ◽  
Vol 50 (3) ◽  
pp. 1605
Author(s):  
E. Argyropoulou ◽  
D. Argialas ◽  
P. Nomikou ◽  
D. Papanikolaou ◽  
M. Dekavalla
Keyword(s):  
Image Analysis ◽  
Automatic Identification ◽  
Fault Surface ◽  
Object Based Image Analysis ◽  
The North ◽  
Object Based ◽  
Morphological Criteria ◽  
North Aegean ◽  
Elevation Model ◽  
Geomorphological Features

This paper is focused on the study of the North Aegean seabed, from a Digital Seabed Elevation Model (DSEM), by employing Object Based Image Analysis (OBIA). The goal is the automatic extraction of geomorphological features based on morphological criteria, in the North Aegean Basin. A Digital Seabed Elevation Model (DSEM) of 150x150 meters resolution was employed. At first, slope gradient, profile curvature, and percentile were derived from this DSEM. Four different layers of segmentation were created in order to extract the final geomorphological classes, discontinuities, faults like and fault surface in the final segmentation of level 4. On previous levels, more geomorphological features were also classified such as continental platform and continental slope. The results were evaluated qualitatively and quantitatively, through a tectonic map which has been created manually based on the analysis of seismic profiles. The results of the comparison of the two methods were satisfactory. Thus, the developed OBIA method is considered successful.

scholarly journals AUTOMATIC IDENTIFICATION OF THE GEOMORPHOLOGIC AND MORPHOTECTONIC FEATURES OF THE SOUTH CRETAN MARGIN, USING OBJECT BASED IMAGE ANALYSIS

2017 ◽  
Vol 50 (3) ◽  
pp. 1633
Author(s):  
R. Kouli ◽  
D. Argialas ◽  
P. Nomikou ◽  
V. Lykousis
Keyword(s):  
Image Analysis ◽  
Automatic Identification ◽  
The South ◽  
Photo Interpretation ◽  
Fault Surface ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Morphological Criteria ◽  
Position Index ◽  
Elevation Model

This paper is focused on the study of the South Cretan Margin, from a Digital Seabed Elevation Model, by employing Object Based Image Analysis. The goal is the automatic extraction of geomorphological and morphotectonic features based on morphological criteria and topological relations. A Digital Seabed Elevation Model of 150x150 meters resolution was employed. At first, slope, curvature, planform curvature, profile curvature and Topographic Position Index were derived from this DSEM. Five different layers of segmentation were created in order to extract the final geomorphological classes, Ptolemy trough, intraslope basins, main basins, small basins, continental shelf, plains, continental slope, escarpments, canyons, spurs, discontinuities, fault like and fault surface. The results were evaluated quantitatively, through the established indices Completeness, Correctness and Overall Quality. For computing these indices, it was necessary to digitize the boundaries of the objects derived by photo-interpretation. Then, the computation of the above indices, took place by comparing the results of digitized photo-interpretation boundaries, to the extracted feature boundaries through OBIA analysis (in shapefile). It is worth noting that, the results of the evaluation are quite satisfactory. Thus, the developed OBIA method is considered successful. 

scholarly journals Object-Based Image Analysis Technique for Gully Mapping Using Topographic Data at Very High Resolution (VHR)

10.29007/shdh ◽  
2018 ◽  
Author(s):  
Antonio Francipane ◽  
Francesca Mussomè ◽  
Giuseppe Cipolla ◽  
Leonardo Noto
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Morphological Characteristics ◽  
Object Based Image Analysis ◽  
Aerial Photogrammetry ◽  
Object Based ◽  
Analysis Technique ◽  
Digital Elevation ◽  
Elevation Model ◽  
Very High

An accurate mapping of gullies is important since they are still major contributors of sediment to streams. Mapping gullies in many areas is difficult because of the presence of dense canopy, which precludes the identification through aerial photogrammetry and other traditional remote sensing methods. Moreover, the wide spatial extent of some gullies makes their identification and characterization through field surveys a very large and expensive proposition.This work aims to develop an object-based image analysis (OBIA) to detect and map gullies based on a set of rules and morphological characteristics retrieved by very high resolution (VHR) imagery. A one-meter resolution LiDAR Digital Elevation Model (DEM) is used to derive different morphometric indexes, which are combined, by using different segmentation and classification rules, to identify gullies. The tool has been calibrated using, as reference, the perimeters of two relatively large gullies that have been measured during a field survey in the Calhoun Critical Zone Observatory (CCZO) area in the Southeastern United States.

scholarly journals New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Jens Oldeland ◽  
Rasmus Revermann ◽  
Jona Luther-Mosebach ◽  
Tillmann Buttschardt ◽  
Jan R. K. Lehmann
Keyword(s):  
Image Analysis ◽  
Plant Species ◽  
Just In Time ◽  
Post Processing ◽  
Tall Grass ◽  
Field Surveys ◽  
Distribution Maps ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

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