Segmentation and Texture Analysis

This paper describes the state of the art in segmentation algorithms of aerial images. Different approaches and object classes are described and their advantages and limitations are shown. First the advantage of multiple input data (e.g., color, infrared, DEM) and the information that can be derived from these sources is discussed. Besides sensor data, “synthetic” input images (e.g., using texture filters) are generated to support the segmentation process. After an optional noise cleaning, primitives are extracted in scale space. This offers the possibility of selecting an optimal resolution depending on the size and shape of an object. Using this resolution, the raw segmentation will be stable and conflicts with other object classes will be reduced. Depending on the class of the object the final extraction has to be selected: Compact artificial objects can be segmented using primitives like areas, lines, or points. Linear objects like roads are similar but the borders are curves and the size is not limited. Arbitrary areas like meadows, forests, or fields have an arbitrary border and are mainly defined by their specific texture. Objects like trees or cars have to be treated in a very specific manner. Finally, different base algorithms for segmentation are discussed: Pixel classification is very simple but lacks the use of context. The extraction of primitives (egdes, lines, area, points) can be used as a basis for a wide class of objects. Texture analysis can be used for a rough segmentation of the image. Specialized operations are useful for the extraction of objects like single trees or to support the interpretation process.