The advantages that a side-scan sonar can offer include large-scale survey areas and high-resolution imagery which can provide the detection and positioning of underwater targets effectively. The purpose of image analysis, classification and positioning in this research was presented by the development of an automated recognition and classification system based on sonographs collected off Kenting area. Major components of the system include gray level co-occurrence matrix method, Baysian classification and cluster analysis. The sonograph classified by the automated recognition and classification system was split into two stages. The first stage divided the seafloor into three categories: (1) Rocky seafloor. (2) Sandy seafloor. (3) Acoustic shadow seafloor. Based on the characteristics of the rocky seafloor, the rocky seafloor was subdivided into five types in the second stage: (1) Flank reef and small independent reef. (2) Smooth reef. (3) Small coral on reef. (4) Coral on independent reef. (5) Large coral on reef. Analysis and proof of the system was conducted by underwater photographs collected off Kenting area in August 4, to 6, 2004. The identification accuracy of the first stage can reach 93% in Shiniuzai area. The characteristic features selected in this research (i.e., entropy and homogeneity) for the classification of various coral reef seafloors was proved adequate and the results was described in map within a Geographic Information System in the second stage. The results of this research illustrated that the rocky area identified in Shiniuzai was 98,863 m2. Due to image resolution restrictions, only 62,199 m2 of the total rocky area could be defined and classified properly. Among them, the flank reef and small independent reef covered an area of 15,954 m2 (26.3%); the smooth reef covered 3,133 m2 (5.0%); the small coral on reef covered 8,021 m2 (12.8%); the coral on independent reef covered 25,504 m2 (40.7%) and the large coral on reef covered 9,587 m2 (15.3%). Key words:side scan sonar,coral reef,gray level co-occurrence matrix

Gray level co-occurrence matrix has proven to be a powerful basis for use in texture classification. Various textural parameters calculated from the gray level co-occurrence matrix help understand the details about the overall image content. The aim of this research is to investigate the use of the gray level co-occurrence matrix technique as an absolute image quality metric. The underlying hypothesis is that image quality can be determined by a comparative process in which a sequence of images is compared to each other to determine the point of diminishing returns. An attempt is made to study whether the curve of image textural features versus image memory sizes can be used to decide the optimal image size. The approach used digitized images that were stored at several levels of compression. GLCM proves to be a good discriminator in studying different images however no such claim can be made for image quality. Hence the search for the best image quality metric continues.

The normal procedure for the detection of underwater stationary targets is mainly by the application of side-scan sonar. In addition, the identification of targets within the side-scan sonar imagery is primarily based on the visual observation of the operator. Due to its complexity and poor effectiveness, the visual observation procedure was gradually been substituted by numerical analysis procedures and programs. The purpose of the current investigation was dedicated to the development of an automatic image analysis program for the detection and identification of cubic concrete artificial reefs (2 m x 2 m x 2m) in the south-western coastal area off Taiwan. The major components and methodologies of the program include: (1)Image acquisition; side-scan sonar at 500 kHz and slant range at 75 m. (2)Feature extraction; grey level co-occurrence matrix. (3)Feature Classification; unsupervised Bayesian classifier. (4)Target identification; cluster analysis. (5)Target properties analysis, includes circumference, area, central coordinates and quantity of the targets. Program verification and optimal parameters determination were conducted with a sonograph (650 ¡Ñ 650 pixels) acquired at the Chey-Ding artificial reef site off Kaohsiung County. Feature functions employed in this program include entropy, homogeneity, and mean value. The identification accuracy can reach 93% at the most. In addition, the number of artificial reefs estimated by the program was within 9 to 20, while the actual number is 15. A realistic evaluation of this program was conducted with a sonograph (2048 ¡Ñ 6050 pixels) acquired at Fang-Liau artificial reef site off Pyngdong County. In addition to the cubic reefs, the targets at this site include cross-shaped artificial reefs with dimensions less than the cubic reefs. The sonograph was divided into smaller blocks with dimensions of 2048 x 550 pixels during evaluation. The results showed that each block can be evaluated based on the value of the seed point obtained by cluster analysis. The seed point which fells between 20.6 and 24.4 indicates that there are cubic reefs existed. Between 15.3 and 17.4 indicates that there are targets with smaller dimensions (i.e., crossed reefs) existed which can not be identified properly. Between 10.1 and 10.9, there is no target existed on the seafloor. The results indicated that the number of targets identified is between 122 and 240. According to the results of this investigation, the automatic image analysis program can improve the detection and identification of stationary targets within side-scan sonar imagery.

In this dissertation, new digital image processing methods for hyperdimensional imagery are developed and experimentally tested on remotely sensed Earth observations and medical imagery. The high dimensionality of the imagery is either inherent due to the type of measurements forming the image, as with imagery obtained with hyperspectral sensors, or the result of preprocessing and feature extraction, as with synthetic aperture radar imagery and digital mammography. In the first study, two omni-directional adaptations of gray level co-occurrence matrix analysis are developed and experimentally evaluated. The adaptations are based on a previously developed rubber band straightening transform that has been used for analysis of segmented masses in digital mammograms. The new methods are beneficial because they can be applied to imagery where the region of interest is either poorly segmented or not segmented. The methods are based on the concept of extracting circular windows s around each pixel in the image which are radially resampled to derive rectangular images. The images derived from the resampling are then suitable for standard GLCM techniques. The methods were applied to both remotely sensed synthetic aperture radar imagery, for the purpose of automated detection of landslides on earthen levees, and to digital mammograms, for the purpose of automated classification of masses as either benign or malignant. Experimental results show the newly developed methods to be valuable for texture feature extraction and classification of un-segmented objects. In the second study, a new technique of using spatial information in spectral band grouping for remotely sensed hyperspectral imagery is developed and experimentally tested. The technique involves clustering the spectral bands based on similarity of spatial features extracted from each band. The newly developed technique is evaluated in automated classification systems that utilize a single classifier and systems that utilize multiple classifiers combined with decision fusion. The systems are experimentally tested on remotely sensed imagery for agricultural applications. The spatial-spectral band grouping approach is compared to uniform band windowing and spectral only band grouping. The results show that the spatial-spectral band grouping method significantly outperforms both of the comparison methods, particularly when using multiple classifiers with decision fusion.

Η χρήση των δεδομένων Ραντάρ Συνθετικού Ανοίγματος (SAR) σε εφαρμογές απομακρυσμένης παρακολούθησης της Γης έχει ήδη αρχίσει να πρωταγωνιστεί τις τελευταίες δεκαετίες. Τα συστήματα SAR με δυνατότητες μεταξύ άλλων συνεχούς λειτουργίας παντός καιρού, ημέρα και νύχτα, προσφέροντας μεγάλη κάλυψη εδάφους και με δυνατότητα λήψης απεικονίσεων πολλαπλών πολώσεων, έχουν αποτελέσει πηγή πολύτιμων πληροφοριών τηλεπισκόπησης. Έτσι, η χρήση των SAR δεδομένων για την ταξινόμηση κάλυψης γης προσελκύει όλο και περισσότερο την προσοχή των ερευνητών και φαίνεται να είναι πολλά υποσχόμενη. Η παρούσα ειδική επιστημονική εργασία έχει στόχο τη μελέτη και ερμηνεία των δεδομένων SAR μέσω επιβλεπόμενης ταξινόμησης, με τη χρήση νευρωνικών δικτύων (Neural Networks). Αφού πρώτα γίνεται εκτενής αναφορά στη τεχνολογία και τα συστήματα SAR, παρουσιάζεται αναλυτικά η πειραματική διαδικασία ταξινόμησης τριών βασικών δομών κάλυψης γης. Τα δεδομένα προέρχονται από το Προηγμένο Ραντάρ Συνθετικού Ανοίγματος (ASAR) του δορυφόρου ENVISAT από τον Ευρωπαϊκό Οργανισμό Διαστήματος και αφορούν στην ευρύτερη περιοχή του Άμστερνταμ. Πριν την διεξαγωγή της ταξινόμησης, τα δεδομένα δέχθηκαν τις απαραίτητες διαδικασίες προ-επεξεργασίας (ραδιομετρική βαθμονόμηση, γεωαναφορά, φιλτράρισμα θορύβου, συμπροσαρμογή). Όσον αφορά τη διαδικασία της ταξινόμησης, εξετάζεται η συμπεριφορά του ταξινομητή του νευρωνικού δικτύου για μεταβολές ποικίλων παραμέτρων, όπως η επιλογή δεδομένων διαφόρων πολώσεων, το πλήθος των νευρώνων κ.α. και ήδη από τα πρώτα πειράματα λαμβάνονται ικανοποιητικά αποτελέσματα. Στη συνέχεια εφαρμόζονται τεχνικές σύνθεσης πληροφορίας (average rule, majority rule) βελτιώνοντας τις επιδόσεις ταξινόμησης. Τέλος, ένα σημαντικό βήμα που εφαρμόζεται στη διαδικασία ταξινόμησης αποτελεί η εξαγωγή χαρακτηριστικών υφής από τις μήτρες συνεμφάνισης φωτεινοτήτων (Gray Level Co-occurrence Matrix-GLCM) και μήκους διαδρομής φωτεινότητας (Gray Level Run Length Matrix-GLRLM). Η χρήση των χαρακτηριστικών αυτών βελτιστοποιεί το σύστημα ταξινόμησης, δίνοντας εξαιρετικά αποτελέσματα. / The use of Synthetic Aperture Radar (SAR) data in remote sensing applications has become a cutting edge technology during the past few decades. The SAR systems have several capabilities, like day & night and all weather operation and they offer large ground coverage with the ability of multi-polarized imagery; therefore, they have proved to be a valuable source of remote sensing data. As a result, the use of SAR data for land cover classification increasingly attracts the attention of researchers and seems to be highly promising. Goal of this master thesis is the study and interpretation of SAR data through supervised classification, with the use of Neural Networks method. First, there is an extensive presentation of SAR systems and technology and then follows the detailed presentation of the experimental classification process for three basic land cover structures. The available data are from the Advanced SAR (ASAR) radar of the ESA ENVISAT satellite and correspond to the Amsterdam city and suburbs. Prior to the classification process, the data have been appropriately pre-processed (radiometric calibration, geocoding, speckle filtering, co-registration). Regarding the classification process, the response of the neural network classifier with the variation of several parameters (e.g. data polarization and number of neurons) is studied and from the initial test already the results were quite satisfactory. Further on, ensemble classifying methods (average rule, majority rule) are applied to improve the classification performance. Finally, as an essential step applied in the classification process is the textural feature extraction from Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). The use of these texture features optimizes the classification system, resulting to an exceptional performance.

In this paper the brief overview of methods available for individual tree stems quality evaluation and tree species classification has been performed. The use of Near Infrared photometry based on conifer’s canopy reflectance measurement in near infrared range of spectrum has been evaluated for the use in autonomous forest harvesting. Photometric method based on the image processing of the bark pattern has been proposed to perform classification between main construction timber tree species in Scandinavia: Norway spruce (Picea abies) and Scots Pine (Pinus sylvestris). Several feature extraction algorithms have been evaluated, resulting two methods selected: Statistical Analysis using gray level co-occurrence matrix and maximally stable extremal regions feature detector. Feedforward Neural Network with Backpropagation training algorithm and Support Vector Machine classifiers have been implemented and compared. The verification of the proposed algorithm has been performed by real-time testing.