In an area of interest- Sivas Basin, Turkey- where most of the units are sedimentary and show similar spectral characteristics, spectral settings of ASTER sensor may not be enough by itself. Therefore, considering other aspects, such as morphological variables, is reasonable in addition to spectral classifiers. The main objective of this study is to test usefulness of integration of spectral analysis and morphological information for geological mapping. Remotely sensed imagery obtained from ASTER sensor is used to classify different lithological units while DEM is used to characterize landforms related to these lithological units. Maximum Likelihood Classification (MLC) is used to integrate data streaming from different sources. The methodology involves integrating the surface properties of the classified geological units in addition to the spectral reflectances. Seven different classification trials were conducted: : 1. MLC using only nine ASTER bands, 2. MLC using ASTER bands and DEM, 3. MLC using ASTER bands and slope, 4. MLC using ASTER bands and plan curvature, 5. MLC using ASTER bands and profile curvature, 6. MLC using ASTER bands and drainage density and finally 7. MLC using ASTER bands and all ancillary data. The results revealed that integrating topographical parameters aid in improvement of classification where spectral information is not sufficient to discriminate between classes of interest. An increase of more than 5% is observed in overall accuracy for the all ancillary data integration case. Moreover more than 10% improvement for most of the classes was identified. However from the results it is evident that the areal extent of the classified units causes constraints on application of the methodology.

The difficulty of gathering information on field and coarse resolution of Landsat images forced to use ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos region. In the first part of the study, coarse vegetation classification is attained by using maximum likelihood method with the help of forest management maps. Canonical Correspondence analysis is used to explore the relationships among the environmental variables and vegetation classes. Discriminant Analysis is used in the second part of the study in two different stages. Firstly Fisher&rsquo / s linear equations for each of the previously defined nine groups calculated and the pixels are included in one of these groups by looking at the probability of that pixel being in that group. In the second stage Distance raster value of maximum likelihood classification is used. Distance raster pixels having a value less than one is accepted as misclassified and replaced with a value of first stage result of that pixel. As a result of this study 19.6 % increase in the overall accuracy is obtained by using the relationships between environmental variables and vegetation distribution.

The aim of this study is to develop a framework for the integration of ancillary topographic information into supervised image classification to improve the accuracy of the classification product. Integration of topographic data into classification is basically through modification of training set in order to provide additional sensitivity to topographical characteristics associated with each land cover class in the study area. Multi-spectral Landsat 7 ETM 30x30 meter bands are the remotely sensed data used in the study. Ancillary topographic data are elevation, slope and aspect derived from 1/25000 scaled topographic map contours. A five-phase methodological framework was proposed for developing procedures for the integration of topographical data into a standard image classification task. Briefly / first phase is the selection of initial class spectral signatures, second phase is analyzing the information content of class spectral signatures and topographical data for a potential relationship, and quantification of the related topographical data. Third phase is the selection of class topographical signatures from the related topographical data. Fourth phase is redefinition of two training sets where one of which includes spectral information only and the other includes both spectral and topographical information. The last phase is classification. Two products were derived where, first product used bands as input and was trained by spectral information only and the second was the product for which bands and topographical data was used as input and it was trained with both spectral and topographical information. Method was applied to image and associated ancillary topographical data covering rural lands mainly composed of agricultural practices and rangelands in Ankara. Method provided an improvement of 10% in overall accuracy for the classification with the integration of topographical data compared to that depended only on spectral data from remotely sensed images.

Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity.       Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.