Title :
Segmentation and classification of remote sensing images using confident marker selection
Author :
Khodadadzadeh, Mahdi ; Ghassemian, Hassen
Author_Institution :
Fac. of Electr. & Comput. Eng., Tarbiat Modares Univ., Tehran, Iran
Abstract :
A new method for segmentation and classification of agricultural remote sensing images is proposed. The method is based on region growing algorithm from confident selected markers. We propose a straightforward spectral-spatial method to choose the most reliable classified pixels in order to define suitable markers. We use probability estimates which are obtained from support vector machine (SVM) classification for computing entropy criterion of each pixel in order to determine spectral confident pixels. Erosion technique is used to extract spatial confident pixels of initial classification map which are the pixels far from the borders (spatial boundaries). By combining these procedures, confident markers are defined and by construction of a minimum spanning forest (MSF) from these markers the final spectral-spatial classification map is obtained. Experimental results show that the proposed method improves dramatically classification accuracy in comparison with pixelwise classification.
Keywords :
agriculture; feature extraction; geophysical image processing; image classification; image resolution; image segmentation; remote sensing; support vector machines; agricultural remote sensing image classification; agricultural remote sensing image segmentation; confident marker selection; entropy criterion; minimum spanning forest; probability estimation; region growing algorithm; spectral confident pixel extraction; spectral-spatial classification map; spectral-spatial method; support vector machine classification; Agricultural remote sensing images; marker selection; minimum spanning forest (MSF); spectral-spatial classification; support vector machine (SVM);
Conference_Titel :
Artificial Intelligence and Signal Processing (AISP), 2011 International Symposium on
Conference_Location :
Tehran
Print_ISBN :
978-1-4244-9833-8
DOI :
10.1109/AISP.2011.5960984