Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent com... more Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.
Object-based classification approaches have been recently employed successfully
in many research ... more Object-based classification approaches have been recently employed successfully in many research studies. These approaches aim to create segments on the image considering spectral similarity of the neighboring pixels, which is known as image segmentation. Segmentation methods use spectral information as well as textural and semantic information of the pixels. It is a fact that parameter setting of segmentation methods is of considerable importance in producing accurate classification results. Therefore, determining optimum values for the parameters is regarded as a critical stage in segmentation processes. In this study, effectiveness and applicability of the segmentation approach was analyzed utilizing a high resolution Quickbird satellite image. Multi-resolution segmentation technique, which has been reported to be a robust method, was employed with its optimal parameters for scale, shape and compactness that were defined after an extensive trail process on the data set. Resulting image object was then used in supervised classification using the nearest neighbor algorithm with fuzzy membership functions. Classification performances produced for different parameter settings were thoroughly analyzed and it was found that parameter setting in segmentation applications produced highly varied classification accuracies. It was also observed that segmentation algorithms could help
Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent com... more Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.
Object-based classification approaches have been recently employed successfully
in many research ... more Object-based classification approaches have been recently employed successfully in many research studies. These approaches aim to create segments on the image considering spectral similarity of the neighboring pixels, which is known as image segmentation. Segmentation methods use spectral information as well as textural and semantic information of the pixels. It is a fact that parameter setting of segmentation methods is of considerable importance in producing accurate classification results. Therefore, determining optimum values for the parameters is regarded as a critical stage in segmentation processes. In this study, effectiveness and applicability of the segmentation approach was analyzed utilizing a high resolution Quickbird satellite image. Multi-resolution segmentation technique, which has been reported to be a robust method, was employed with its optimal parameters for scale, shape and compactness that were defined after an extensive trail process on the data set. Resulting image object was then used in supervised classification using the nearest neighbor algorithm with fuzzy membership functions. Classification performances produced for different parameter settings were thoroughly analyzed and it was found that parameter setting in segmentation applications produced highly varied classification accuracies. It was also observed that segmentation algorithms could help
Uploads
Papers by Merve Yildiz
an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies.
Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.
in many research studies. These approaches aim to create segments on the image
considering spectral similarity of the neighboring pixels, which is known as image
segmentation. Segmentation methods use spectral information as well as textural
and semantic information of the pixels. It is a fact that parameter setting of
segmentation methods is of considerable importance in producing accurate
classification results. Therefore, determining optimum values for the parameters is
regarded as a critical stage in segmentation processes. In this study, effectiveness
and applicability of the segmentation approach was analyzed utilizing a high
resolution Quickbird satellite image. Multi-resolution segmentation technique,
which has been reported to be a robust method, was employed with its optimal
parameters for scale, shape and compactness that were defined after an extensive
trail process on the data set. Resulting image object was then used in supervised
classification using the nearest neighbor algorithm with fuzzy membership
functions. Classification performances produced for different parameter settings
were thoroughly analyzed and it was found that parameter setting in segmentation
applications produced highly varied classification accuracies. It was also observed
that segmentation algorithms could help
an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies.
Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.
in many research studies. These approaches aim to create segments on the image
considering spectral similarity of the neighboring pixels, which is known as image
segmentation. Segmentation methods use spectral information as well as textural
and semantic information of the pixels. It is a fact that parameter setting of
segmentation methods is of considerable importance in producing accurate
classification results. Therefore, determining optimum values for the parameters is
regarded as a critical stage in segmentation processes. In this study, effectiveness
and applicability of the segmentation approach was analyzed utilizing a high
resolution Quickbird satellite image. Multi-resolution segmentation technique,
which has been reported to be a robust method, was employed with its optimal
parameters for scale, shape and compactness that were defined after an extensive
trail process on the data set. Resulting image object was then used in supervised
classification using the nearest neighbor algorithm with fuzzy membership
functions. Classification performances produced for different parameter settings
were thoroughly analyzed and it was found that parameter setting in segmentation
applications produced highly varied classification accuracies. It was also observed
that segmentation algorithms could help