Evaluating efficiency of object-based classification techniques used to extract land use from IKONOS satellite imageries

With the advent of remote sensing technology, huge volume of remotely sensed data is now availablein different areas. As the fastest and the most cost-efficient method, satellite data is available for both researchers and responsible authorities seeking to produce land use (LU) maps. Compared to traditional methods, object based image analysis (OBIA) techniques use more comprehensive datasets,including geometric information (shape and placement of phenomena), digital elevation models, andvarious spectralindicesfor LU classification.Therefore, different OBIA methods have been widely used forclassification of satellite imageriesin different regions. Despite large amount of researches performed in this area, little attention has been paid to the systematic comparison ofdifferent object-based methods. Therefore, examining different techniques used for object-based processing of satellite imageries in diffrent situations can be considered as an appropriate research field for researchers.  The present studyexamines some powerful OBIA classification techniques such as threshold, nearest neighbor algorithm and fuzzy object based classification to determine the most suitable OBIA algorithm for classification of Ikonos satellite images.  

An Ikonos satellite imagery was used in this studywhich included red, green, blue and near-infrared bandswith spatial resolution of 4 m and a1 m resolutionpanchromatic band.Object based classification can be implemented in three general phases: segmentation, classification, and accuracy assessment.The present study has appliedmulti-resolution segmentation method in the segmentation phase. Three techniques ofthreshold, nearest neighbor algorithm and fuzzy based OBIA were also used for classification. 

The present study takes advantage of various features to extract land use classesfrom Ikonos satellite imageswith high level of accuracy.Textual information (Grey Level Co-occurrenceMatrix), mean of the imagery’s spectral bands, geometry (shape, density and asymmetry), and normalized difference vegetation index (NDVI)were among these features.Compared to threshold method,nearest neighbor algorithm withoverall accuracy of 92% and kappacoefficient of 0.9hada higher level of accuracy.Also, FOS algorithm was used to optimize the nearest neighbor technique. This algorithm optimizes intervals between the training samples using secondary information provided by the user.The eighteenth dimension, which contains the mean of spectral bands3 and 4, vegetation index, brightness, length to width ratio, indices of shape, compactness, asymmetry, texture information (homogeneityand contrast), were determined by FOS algorithmas the best dimension for extracting each LU classes. Finally,featuresproposed by FOS algorithm were used for image classification in nearest neighbor method.This optimizing process is considered to be one of the main reasons for superior performance ofnearest neighbor technique compared to threshold method.  

In this research, three OBIA methods including threshold technique, nearest neighbor algorithm and fuzzy based OBIA algorithm were compared based on their capability in producing land use map from Ikonos satellite image. Identical ground control pointsof the study areawere used to classify and compare the results of these three OBIA classification methods.Finally, the best classification algorithmwas determinedbased on thevalues of accuracy assessment metrics including overall accuracy and kappa coefficient. Results indicate thatwith overall accuracy of 97%, and kappa coefficient of 0.95, fuzzy based OBIA classification algorithm has thehighest accuracy as compared to nearest neighbor algorithm and threshold method. Generally, the accuracy of fuzzy based OBIA classification method largely depends on the selection of appropriateclassification parameters and suitablealgorithm to obtain membership degrees.Investigating membership degree of effective parameters in the classification and using parameters with maximum degree of membership are considered to be two main reasons for achieving this high accuracy. Results of the present study indicate that fuzzy based OBIA techniqueis the best algorithm for classification ofIKONOS satellite images in the study area, andareas with similar conditions. This findingcanguide researchers and organizations producingLU map from IKONOS satellite imagery. Finally, investigating different techniques using satellite imageries (imageries with different spatial resolution, and received from areas with different land uses) is considered to be an appropriate area of study for OBIA researches.