Agriculture has an important role in Turkey / hence automated approaches are crucial to maintain sustainability of agricultural activities. The objective of this research is to classify eight crop types cultivated in Karacabey Plain located in the north-west of Turkey using multi-temporal Kompsat-2 and Envisat ASAR satellite data. To fulfill this objective, first, the fused Kompsat-2 images were segmented separately to define homogenous agricultural patches. The segmentation results were evaluated using multiple goodness measures to find the optimum segments. Next, multispectral single-date Kompsat-2 images with the Envisat ASAR data were classified by MLC and SVMs algorithms. To combine the thematic information of the multi-temporal data set, probability maps were generated for each classification result and the accuracies of the thematic maps were then evaluated using segment-based manner. The results indicated that the segment-based approach based on the SVMs method using the multispectral Kompsat-2 and Envisat ASAR data provided the best classification accuracies. The combined thematic maps of June-August and June-July-August provided the highest overall accuracy and kappa value around 92% and 0.90, respectively, which was 4% better than the highest result computed with the MLC method. The produced thematic maps were also evaluated based on field-based manner and the analysis revealed that the classification performances are directly proportional to the size of the agricultural fields.

ABSTRACT DECISION TREE CLASSIFICATION OF MULTI-TEMPORAL IMAGES FOR FIELD-BASED CROP MAPPING Sencan, Se&ccedil / il M. Sc., Department of Geodetic and Geographic Information Technologies Supervisor: Assist. Prof. Dr. Mustafa T&uuml / rker August 2004, 125 pages A decision tree (DT) classification approach was used to identify summer (August) crop types in an agricultural area near Karacabey (Bursa), Turkey from multi-temporal images. For the analysis, Landsat 7 ETM+ images acquired in May, July, and August 2000 were used. In addition to the original bands, NDVI, PCA, and Tasselled Cap Transformation bands were also generated and included in the classification procedure. Initially, the images were classified on a per-pixel basis using the multi-temporal masking technique together with the DT approach. Then, the classified outputs were applied a field-based analysis and the class labels of the fields were directly entered into the Geographical Information System (GIS) database. The results were compared with the classified outputs of the three dates of imagery generated using a traditional maximum likelihood (ML) algorithm. It was observed that the proposed approach provided significantly higher overall accuracies for the May and August images, for which the number of classes were low. In May and July, the DT approach produced the classification accuracies of 91.10% and 66.15% while the ML classifier produced 84.38% and 63.55%, respectively. However, in August nearly the similar overall accuracies were obtained for the ML (70.82%) and DT (69.14%) approaches. It was also observed that the use of additional bands for the proposed technique improved the separability of the sugar beet, tomato, pea, pepper, and rice classes.

We used RUSI (rehabilitative ultrasound imaging) for measurement of abdominal and back muscle in different loading modes. Methods: We used diagnostic ultrasonography imaging for taking linear measurement of trunk muscles. We measured anterioposterior (AP) dimensions of lateral abdominal wall muscles- m. OE, m.OI, m.TrA and cross-section area (CSA) of lumbar m. multifidus. We compared two groups of sports-floorball players and sportsman using climbing and hanging (climbers, aerialists). We measured positions with compressive force (kneeling on all four with lifted knees) and with traction load (hang with upper limbs with flexion of lower limbs-with leg support and without). Study is made on 50 volunteers. Results: The pattern of thickness of abdominal muscles is same in all positions in both groups. The lowest is always AP thickness of m. TrA, wider is m. OE and the widest always m. OI. The resting thickness are in both groups almost in all cases the lowest. AP thickness in m. TrA in floorball players is only exception, there is lowest in hang without legs support. In all other case sis resting position always lowest. For m. OE are results same for both climbers and floorball players-the lowest thickness is in hang with legs support (floorball players 0,84 cm, climbers 0,87), greater activationis...

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