A stereo image matching algorithm based on areas, multi-scale regions, edges and recognized objects, like buildings and roads is discussed. The algorithm includes five key parts: (1) Multi-scale image segmentation; (2) Object recognition/classification; (3) Matching using area, multi-scale regions, edges and recognized objects; (4) Bare earth surface reduction; (5) and manual editing of the automated matching result. The image is segmented first at different scales. Each scale represents objects at different abstraction levels.

Suburban sprawl has been identified as the most important social and environmental issue facing Americans in their community. Suburban development consumes green space, widens urban fringes, increases impervious surface areas (ISA), and puts pressure on environmentally sensitive inland and coastal areas. Urban runoff, mostly through ISA, is the leading source of pollution in the Nation’s estuaries, lakes, and rivers. ISA can serve as a key environmental indicator due to its impacts on water systems and its role in transportation and concentration of pollutants.

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.

In this paper, a new approach to road network extraction from multi-spectral (MS) imagery is presented. The proposed approach begins with an image segmentation using a spectral clustering algorithm. This step focuses on the exploitation of the spectral information for feature extraction. The road cluster(s) is automatically identified using a fuzzy classifier based on a set of predefined membership functions for road surfaces and the corresponding normalized digital numbers in each multi-spectral band.

Impervious surface area is a key indicator of environmental quality. Satellite remote sensing of impervious surface has focused on subpixel analysis via various forms of statistical estimation, subpixel classification, and spectral mixture analysis, using medium resolution Landsat TM or ETM+ data. Maps of impervious surface area from these studies provide useful inputs to planning and management activities at city to regional scales. However, for local studies, large-scale, higher resolution maps are preferred.

Federal Clean Water Act requires that states develop Total Maximum Daily Loads (TMDLs) for water bodies. Once the state has developed an inventory of TMDLs, it is required to provide public notice of the report and have it approved by the Environmental Protection Agency. The South Dakota Department of Environment and Natural Resources (DENR) is using the USDA’s annualized Agricultural Non-Point Source Pollution Model to determine what land use changes are required to meet TMDL goals (South Dakota DENR, 2006).

The objective of this research was the development of a generic image segmentation algorithm, as a low level processing part of an integrated object-oriented image analysis system. The implemented algorithm is called Mseg and can be described as a region merging procedure. The first primitive object representation is the single image pixel. Through iterative pairwise object fusions, which are made at several iterations, called passes, the final segmentation is achieved.

Wildfires continue to put pressure on planning and mitigation efforts making the ability to map fire fuels and risks increasingly important. This project attempts to map areas on state, private, and other federal lands for this purpose and has focused on the development of advanced digital mapping methodologies to support fire fuels mapping. The fusion of advanced image classification techniques with high-resolution satellite data has proven to provide costeffective and accurate inventories of fire fuels and associated risks in the WUI.

Urban areas are the economic and social centers of our modern life. These areas are characterized by a number of dense artificial man made objects making these regions very complex and efficient. Urbanized areas have been developed to that extend we can observe today over the last 100 years. However, people living in those regions are still ‘sophisticated and highly evolved animals’ and truly a part of nature. As a consequence, a natural environment is still necessary for feeling comfortable.

In arid regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material. It is important that both types of vegetation can be quantified for land management purposes and for assessing the nutritional value of grasses. Traditional ground sampling procedures are commonly used but are time consuming. Our goal was to develop an image analysis approach for separating and quantifying green and senescent grasses in the same plot using very high resolution ground photography.