Kong-Hyun Yun

Shadow-Effect Correction in Aerial Color Imagery

Due to the existence of shadows, especially in urban environments, it is difficult to extract semantic information from aerial and high-resolution satellite images. In this paper, an efficient method of correcting shadow effects using multisource data sets in aerial color images is proposed. The proposed method has three steps. First, it accurately detects the shadowed regions using the image geometry and the solar position of the image acquisition data. Then, the detected shadowed regions are segmented according to land surface type. Finally, the shadow effects of the segmented regions are corrected by directly comparing the same nonshadow features with the segmented shadows. In the application part of this paper, the proposed techniques were applied in the extraction of an asphalt road from an image.

Analysis of image fusion methods using IKONOS imagery

In this paper, efforts were made to merge IKONOS panchromatic image with multispectral images using wavelet transform, Intensity-Hue-Saturation (IHS), multiplicative, and Principal Components Analysis (PCA) methods. Numerical comparisons were made to evaluate the effect of different fusion methods on the distortion of spectral characteristics. Likewise, analysis was done on different image fusion results based on land surface materials. Finally, the results of the building outline extraction from fused images and panchromatic image were compared. They show that the extraction of the building boundary from the fused image is better than from the panchromatic image in terms of boundary connection.

Rigorous sensor modeling of early reconnaissance CORONA imagery for monitoring urban growth

We describe geometric modeling processes to remove the severe panoramic distortion of old era CORONA KH4B imagery. We implement the modified collinear equation and rational function model. We can obtain less than 2 pixel horizontal and vertical accuracy. We successfully create a digital elevation model of a test site and orthoimages. With the result of sensor modeling an urban expansion study is accomplished using more recent SPOT imagery.

Road boundary extraction using shadow path reconstruction in urban areas

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a road boundary extraction technique that combines information from aerial color image and LIght Detection And Ranging (LIDAR) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LIDAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadowpath reconstruction algorithms. After that, road boundary extraction is implemented by segmentation, edge detection, and edge linking method. Finally, road boundary lines are extracted as vector data by vectorization technique. The experimental results show that this approach is effective and great potential advantages.

Correction of building height effect using LIDAR and GPS

Correction of building height effects is a critical step in image interpretation from aerial imagery in urban area. In this paper, an efficient scheme to correct building height effects from aerial color imagery using multi source data sets is presented. The following steps have been performed to remove the shadow effect from aerial color imagery. First, the shadow regions of the orthorectified aerial color image are precisely located using the solar position and the heights of ground objects derived from LIDAR (Light Detection and Ranging) data. The shadow area is composed of many different ground features. To accurately recover the original spectral information the step for accurate segmentation of shadow regions needed. This step has been performed by utilizing the existing digital map which contains surface cover information. To assign correction information corresponding to the surface features on the shadowed area, correction factor needed to be modeled. For this three basic assumptions are proposed and comparison between the context region and the same non-shadowed context region is made. Finally, the shadow-effect-corrected image is generated by using the correction factor obtained from the previous step.