Detecting informal settlements might be one of the most challenging tasks within urban remote sensing. This phenomenon occurs mostly in developing countries. In order to carry out the urban planning and development tasks necessary to improve living conditions for the poorest world-wide, an adequate spatial data basis is needed (see Mason, O. S. & Fraser, C. S., 1998). This can only be obtained through the analysis of remote sensing data, which represents an additional challenge from a technical point of view.

Multispectral classification, one of the most commonly used methods for mapping burned areas, is based on the spectral properties of different classes of interest and employs special algorithms designed to perform various types of spectral analysis. However, the use of these classifications has been repeatedly reported to create confusion between burned areas and nonvegetation categories, especially water bodies and shaded areas. As a result of the aforementioned, spectral based classification methods cannot be used operationally for the mapping of burned areas from satellite images.

Pixel-based classifications have difficulty adequately or conveniently exploiting expert knowledge or contextual information. Object-based image-processing techniques overcome these difficulties by first segmenting the image into meaningful multipixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels. The segments (objects) are assigned classes using fuzzy logic and a hierarchical decision key. To date, the main drawback has been the lack of effective software.

Most traditional pixel-based classification approaches are based exclusively on the digital number of the pixel itself. Thereby only the spectral information is used for the classification. The situation comes into worse when extracting certain interested features only. As a result, it produces the salt-and-pepper-effect pseudocolor layer. An object-oriented classification is suggested in order to overcome the limitations stated above. The existing software, eCognition allows the polygon based classification process.

Sierra de Artega, in Northern México, is a mountainous area that represents an important biological area at regional level due its great diversity in species and endemisms. This area has a wide elevation range from 800 to 3500 meters above sea level, which results in an interesting mosaic of plant communities along the study area. These communities are Pine forest, Oak forest, Pinus-Abies-Pseudotsuga forest, Pine-Oak forest, Submontane scrub, Desert scrub, Chaparral and some Grasslands.

Rapid expansion of coastal aquacultre/shrimp farming in several countries has necessitated the inventory and monitoring of shrimp farms. these are essential tools for decision making on aquaculture development, including regulatory laws,environmental protection and revenue collection. In the context of aquaculture development policies of respective governments, much attention are focused on the identification and classification of shrimp farms, often located in remote areas.

There is a need to automate terrain feature mapping so that to make the process more objective and less time consuming by using proper feature extraction techniques. The objective of this study was the use of object-oriented image analysis methods for the automatic extraction of alluvial fan terrain units. The study area was located in the Death Valley, Nevada, USA. The data used included an ASTER L1 satellite image and the 1o Digital Elevation Model.

The production of a preliminary land cover map by spatial analysis, which integrates remotely sensed imagery with GIS, may provide a useful tool in the selection of sites for vegetation mapping and other natural resource inventory surveys. eCognition, an object-oriented image analysis software package, was used to produce a preliminary land cover map of Groote Eylandt with 20 land cover classes prior to the commencement of vegetation surveys.

Object-oriented classification has shown a great potential in the classification of very high resolution satellite images, such as QuickBird and Ikonos. In the object-oriented classification, object segmentation is a crucial process and it significantly influences the classification results. Current techniques heavily rely on the operator’s experience to find appropriate segmentation parameters for achieving an acceptable classification result, which is a labour intensive and time consuming work.

This is a review of techniques for separating land cover classes for a change detection project in the Central Valley, California. The Mid Pacific Region of the U.S. Bureau of Reclamation is in the midst of the second cycle in monitoring change over major portions of the Central Valley. In agricultural areas, a considerable amount of spectrally identified change is due to cultural practices. This has led to confusion in separating change due to agricultural practice from change between categories of bare ground, grassland, forbs, and shrub land.