|
|
|
Object oriented classification for land cover mapping
Shattri Mansor, Wong Tai Hong and Abdul Rashid Mohamed Shariff
Spatial and Numerical Modeling Laboratory
Institute of Advanced Technology, University Putra Malaysia,
43400 Serdang, Selangor, Malaysia
shattri@putra.upm.edu.my
Introduction
Classification based on pixel-based approaches to image analysis is limited nowadays. Typically, they have considerable difficulties dealing with the rich information content of Very High Resolution (VHR) or moderate resolution such as Landsat TM or Spot data; they produce a characteristic, inconsistent salt-and-pepper classification, and they are far from being capable of extracting objects of interest. Therefore, the vast majority of operational projects can be realized only by means of massive human interaction. Due to this, application of new type supervised classification process is now bringing into polygon base. It is necessary to make their contents manageable, which requires one or more preferably meaningful image segmentations. Additional information such as from criteria, textual or contextual information of the segments then must be describable in an appropriate way to derive improved classification results.
Multiresolution Segmentation
The concept behind eCognition is that important semantic information necessary to interpret an image is not represented in single pixels, but in meaningful image objects and their mutual relationships (Martin Baatz et. al, 2001). The eCognition software performs a first automatical processing - segmentation - of the imagery. This results to a condensing of information and a knowledge-free extraction of image objects. The formation of the objects is carried out in a way that an overall homogeneous resolution is kept. The segmentation algorithm does not only rely on the single pixel value, but also on pixel spatial continuity (texture, topology). The formatted objects have now not only the value and statistic information of the pixels that they consist. They carry also texture, form (spatial features) and topology information in a common attribute table. (Ioannis Manakos, 2001) The organized images objects carry not only the value and statistical information of the pixels of which they consists, but also information on texture and shape as well as their position within the hierarchical network (Ambiente Humano, 2000). The basic difference, especially when compared to pixel-based procedures, is that object oriented analysis does not classify single pixels, but rather image objects which are extracted in a previous image segmentation step.
Supervised Classification
eCognition supports different supervised classification techniques and different methods to train and build up a knowledge base for the classification of image objects. The frame of knowledge base for the analysis and classification of image objects is the so-called class hierarchy. It contains all classes of a classification scheme. The classes can be grouped in a hierarchical manner allowing the passing down of class descriptions to child classes on the one hand, and meaningful semantic grouping of classes on the other. This simple hierarchical grouping offers an astonishing range for the formulation of image semantics and for different analysis strategies. The user interacts with the procedure and based on statistics, texture, form and mutual relations among objects defines training areas. The classification of an object can then follow the "hard" nearest neighbourhood method or the "soft" method using fuzzy membership functions. Multilevel segmentation, context classification and hierarchy rules are also available (Ioannis Manakos, 2001) By classifying "neighborhoods" in a large-segment level, and "forest" or "impervious" in a small-segment level within the "neighborhood" larger segments, classes such as turf-and-tree and residential could be identified (Emily Wilson1 and Dan Civco, 2002). Class descriptions are performed using a fuzzy approach of nearest neighbor or by combinations of fuzzy sets on object features, defined by membership functions. Whereas the first supports an easy click and classify approach based on marking typical objects as representative samples, the later allows inclusion of concepts and expert knowledge to define classification strategies (Martin Baatz et. al, 2001).
|
|
|