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

Kök, Emre Hamit
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.


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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 ...
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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...
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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.