ABSTRACT:The objective of this research is the automatic extraction of the road network in a scene of the urban area from a high resolution digital surface model (DSM). Automatic road extraction and modeling from remote sensed data has been studied for more than one decade. The methods vary greatly due to the differences of data types, regions, resolutions et al. An advanced automatic road network extraction scheme is proposed to address the issues of tedium steps on segmentation, recognition and grouping. It is on the basis of a geometric road model which describes a multiple-level structure. The 0-dimension element is intersection. The 1-dimension elements are central line and side. The 2-dimension element is plane, which is generated from the 1-dimension elements. The key feature of the presented approach is the cross validation for the three road elements which goes through the entire procedure of their extraction. The advantage of our model and method is that linear elements of the road can be derived directly, without any complex, non-robust connection hypothesis. An example of Japanese scene is presented to display the procedure and the performance of the approach.
ABSTRACT:Aerosol Optical Depth (AOD) is one of the most important parameters in the atmospheric correction of remote sensing images. We present a new method of per pixel AOD retrieval using the imagery of Landsat8. It is based on Second Simulation of the Satellite Signal in the Solar Spectrum (6S). General dark target method takes dense vegetation pixels as dark targets and derives their 550nm AODs directly from the LUT, and interpolates the AODs of other pixels according to spatial neighbourhood using those of dark target pixels. This method will down estimate the AOD levels for urban areas. We propose an innovative method to retrieval the AODs using multiple temporal data. For a pixel which has nothing change between the associated time, there must exists an intersection of surface albedo. When there are enough data to find the intersection it ought to be a value that meet the error tolerance. In this paper, we present an example of using three temporal Landsat ETM+ image to retrieve AOD taking Beijing as the testing area. The result is compared to the commonly employed dark target algorithm to show the effectiveness of the methods.
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