Developing an integrated system for semi-automated segmentation of remotely sensed imagery

Download

index.pdf

Date

2005

Author

Kök, Emre Hamit

Metadata

Show full item record

Item Usage Stats

183
views

107
downloads

Classification of the agricultural fields using remote sensing images is one of the most popular methods used for crop mapping. Most recent classification techniques are based on per-field approach that works as assigning a crop label for each field. Commonly, the spatial vector data is used for the boundaries of the fields for applying the classification within them. However, crop variation within the fields is a very common problem. In this case, the existing field boundaries may be insufficient for performing the field-based classification and therefore, image segmentation is needed to be employed to detect these homogeneous segments within the fields. This study proposed a field-based approach to segment the crop fields in an image within the integrated environment of Geographic Information System (GIS) and Remote Sensing. In this method, each field is processed separately and the segments within each field are detected. First, an edge detection is applied to the images, and the detected edges are vectorized to generate the straight line segments. Next, these line segments are correlated with the existing field boundaries using the perceptual grouping techniques to form the closed regions in the image. The closed regions represent the segments each of which contain a distinct crop type. To implement the proposed methodology, a software was developed. The implementation was carried out using the 10 meter spatial resolution SPOT 5 and the 20 meter spatial resolution SPOT 4 satellite images covering a part of Karacabey Plain, Turkey. The evaluations of the obtained results are presented using different band combinations of the images.

Subject Keywords

Information technology.

URI

http://etd.lib.metu.edu.tr/upload/12606107/index.pdf
https://hdl.handle.net/11511/15114

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Geo-spatial object detection using local descriptors Aytekin, Çağlar; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2011) There is an increasing trend towards object detection from aerial and satellite images. Most of the widely used object detection algorithms are based on local features. In such an approach, first, the local features are detected and described in an image, then a representation of the images are formed using these local features for supervised learning and these representations are used during classification . In this thesis, Harris and SIFT algorithms are used as local feature detector and SIFT approach is ...
Offshore oil slick detection with remote sensing techniques Akar, Sertaç; Süzen, Mehmet Lütfi; Department of Geological Engineering (2007) The aim of this thesis is to develop a methodology for detection of naturally occurring offshore oil slicks originating from hydrocarbon seeps using satellite remote sensing techniques. In this scope, Synthetic Aperture Radar (SAR) imagery has been utilized. Case study area was Andrusov High in the Central Black Sea. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface. They form thin oil films on the sea surface called oil slicks. P...
Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data Erdogdu, Utku; TAN, MEHMET; Alhajj, Reda; Polat, Faruk; Rokne, Jon; Demetrick, Douglas (Inderscience Publishers, 2013-01-01) The availability of enough samples for effective analysis and knowledge discovery has been a challenge in the research community, especially in the area of gene expression data analysis. Thus, the approaches being developed for data analysis have mostly suffered from the lack of enough data to train and test the constructed models. We argue that the process of sample generation could be successfully automated by employing some sophisticated machine learning techniques. An automated sample generation framewo...
Characterization of tight-gas sand reservoirs from horizontal-well performance data using an inverse neural network Kulga, B.; Artun, E.; Ertekin, T. (Elsevier BV, 2018-11-01) Characterization of a tight-gas sand formation using data from horizontal wells at isolated locations is challenging due to the inherent heterogeneity and very low permeability characteristics of this class of resources. Furthermore, characterizing the uncontrollable hydraulic-fracture properties along the horizontal wellbore requires financially demanding and time-consuming operations. In this study, a reservoir characterization model for tight-gas sand reservoirs is developed and tested. The model describ...
A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model Yılmaz, Koray Kamil; Wagener, Thorsten (American Geophysical Union (AGU), 2008-09-11) Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictive uncertainty in the model results. Although a priori parameter estimates derived from observable watershed characteris...

Citation Formats

E. H. Kök, “Developing an integrated system for semi-automated segmentation of remotely sensed imagery,” M.S. - Master of Science, Middle East Technical University, 2005.