Automated classification of landform elements using object-based image analysis

Geomorphology ◽  
2006 ◽  
Vol 81 (3-4) ◽  
pp. 330-344 ◽  
Author(s):  
Lucian Drăguţ ◽  
Thomas Blaschke
Keyword(s):  
Image Analysis ◽  
Automated Classification ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province

AGRIFOR ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Agus Sofyan
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Aerial Photo ◽  
Recording Time ◽  
Object Based Image Analysis ◽  
Optical Sensitivity ◽  
East Kalimantan ◽  
Object Based ◽  
Open Land

Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) technique and calculate the accuracy and accuracy, land cover classification by using Objeck Based Image (OBIA) analysis through examination of field conditions.classifying land cover, the classification includes shrubs, young shrubs, plantations (oil palms), shrubs, mines, open land, roads and water bodies with Accuracy of Overcome 0.86.

scholarly journals OBJECT-BASED IMAGE ANALYSIS FOR HISTORIC MAPS CLASSIFICATION

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 247-254 ◽  
Author(s):  
P. Zatelli ◽  
S. Gobbi ◽  
C. Tattoni ◽  
N. La Porta ◽  
M. Ciolli
Keyword(s):  
Image Analysis ◽  
Maximum Likelihood ◽  
Confusion Matrix ◽  
Added Value ◽  
Maximum Likelihood Classification ◽  
Grass Gis ◽  
Object Based Image Analysis ◽  
The North ◽  
Object Based

<p><strong>Abstract.</strong> Heritage maps represent fundamental information for the study of the evolution of a region, especially in terms of landscape and ecologic features. Historical maps present two kinds of hurdle before they can be used in a modern GIS: they must be geometrically corrected to correspond to the datum in use and they must be classified to exploit the information they contain. This study deals the latter problem: the Historical Cadaster Map, created between 1851 and 1861, for the Trentino region in the North of Italy is available as a collection of maps in the ETRS89/UTM 32N datum. The map is a high resolution scan (230 DPI, 24 bit) of the original map and has been used in several ecological studies, since it provides detailed information not only about land property but also about land use. In the past the cadaster map has been manually digitized and for each area a set of attributes has been recorded. Since this approach is time consuming and prone to errors, automatic and semi-automatic procedures have been tested. Traditional image classification techniques, such as maximum likelihood classification, supervised or un-supervised, pixelwise and contextual, do not provide satisfactory results for many reasons: map colors are very variable within the same area, symbols and characters are used to identify cadaster parcels and locations, lines, drawn by hand on the original map, have variable thickness and colors. The availability of FOSS tools for the Object-based Image Analysis (OBIA) has made possible the application of this technique to the cadaster map. This paper describes the use of GRASS GIS and R for the implementation of the OBIA approach for the supervised classification of the historic cadaster map. It describes the determination of the optimal segments, the choice of their attributes and relevant statistics, and their classification. The result has been evaluated with respect to a manually digitized map using Cohens Kappa and the analysis of the confusion matrix. The result of the OBIA classification has also been compared to the classification of the same map using maximum likelihood classification, un-supervised and supervised, both pixelwise and contextual. The OBIA approach has provided very satisfactory results with the ability to automatically remove the background and symbols and characters, creating a ready to be used classified map. This study highlights the effectiveness of the OBIA processing chain available in the FOSS4G ecosystem, and in particular the added value of the interoperability between GRASS GIS and R.</p>

scholarly journals Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data

2021 ◽  
Vol 13 (15) ◽  
pp. 2913
Author(s):  
Mariacristina Prampolini ◽  
Lorenzo Angeletti ◽  
Giorgio Castellan ◽  
Valentina Grande ◽  
Tim Le Bas ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mediterranean Sea ◽  
Adriatic Sea ◽  
Benthic Habitat ◽  
Automated Classification ◽  
Biological Communities ◽  
Acoustic Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Reflectivity Data

A huge amount of seabed acoustic reflectivity data has been acquired from the east to the west side of the southern Adriatic Sea (Mediterranean Sea) in the last 18 years by CNR-ISMAR. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of them has never been carried out. Here, we aimed at coherently interpreting acoustic data images of the seafloor to produce a benthic habitat map of the southern Adriatic Sea showing the spatial distribution of substrates and biological communities within the basin. The methodology here applied consists of a semi-automated classification of acoustic reflectivity, bathymetry and bathymetric derivatives images through object-based image analysis (OBIA) performed by using the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each cruise dataset separately, then classified and validated through comparison with bottom samples, images, and prior knowledge of the study areas.

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