Automatic aerial image interpretation is one of
the new rising high-tech application fields, and it’s
proverbially applied in the military domain. This survey
paper compares a different approach for target detection and
classification of objects by texture clustering and structure
features extraction. By clustering the texture feature effective
image segmentation is achieved and thus obtain the structure
features of target objects. Typical man-made objects
including airplanes, tank, ships, mines and vehicles in
complex natural background can be detected.
Arya Raj A.K : received her B.Tech (Computer Science &
Engineering) degree from Cochin University of Science &
Technology in 2008. She worked at College of Engineering
Trivandrum as Lecturer. She is currently pursuing her M.Tech
Degree in Computer Science & Engineering from University of
Kerala.
Radhakrishnan B : is currently working as Asst. Professor in
Computer Science & Engineering department. He has more
than 14 years experience in teaching and has published
papers on data mining and image processing. His research
interests include image processing, data mining, image mining
Target Detection
DCT
Morphological Enhancing
Texture Clustering
Segmentation
Structure Feature Extraction
This survey paper proposes an approach for detecting
targets in the military field. Target objects are detected by
clustering the texture feature and then extracting the
geometric structure features. During the texture feature
extraction process, we compare the segmentation result of
histogram-based and Tamura-based texture features. FCM
is chosen as the clustering algorithm. From the study of
different segmentation methods we prefer Markov
Random Field method because accurate segmentation was
achieved on seabed types where other models failed. For
geometric structure feature extraction edges of the target
objects are detected. Canny and Tupin edge detection
method is compared and from this survey Tupin is
considered better than Canny.
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