Object based Information Extraction from High Resolution Satellite Imagery using eCognition

High resolution images offer rich contextual information, including spatial, spectral and contextual information. In order to extract the information from these high resolution images, we need to utilize the spatial and contextual information of an object and its surroundings. If pixel based approaches are applied to extract information from such remotely sensed data, only spectral information is used. Thereby, in Pixel based approaches, information extraction is based exclusively on the gray level thresholding methods. To extract the certain features only from high resolution satellite imagery, this situation becomes worse. To overcome this situation an object-oriented approach is implemented. This paper demonstrated the concept of objectoriented information extraction from high resolution satellite imagery using eCognition software, allows the classification of remotely-sensed data based on different object features, such as spatial, spectral, and contextual information. Thus with the object based approach, information is extracted on the basis of meaningful image objects rather than individual gray values of pixels. The test area has different discrimination, assigned to different classes by this approach: Agriculture, built-ups, forest and water. The Multiresolution segmentation and the nearest neighbour (NN) classification approaches are used and overall accuracy is assessed. The overall accuracy reaches to 97.30% thus making it an efficient and practical approach for information extraction.