Extraction and Utilization of Geometrical and Contextual Information in very High Resolution IKONOS Satellite Imagery
Annie Hui, Soo Chin Liew, Leong Keong Kwoh, and Hock Lim
Centre for Remote Imaging, Sensing and Processing, National University of Singapore
Blk. SOC1 Level 2, Lower Kent Ridge Road, Singapore 119260
Tel: (65) 8746557 Fax: (65) 7757717 Email: crshuia@nus.edu.sg
Key Words
IKONOS satellite, very high resolution imagery, plantations, linear features, building height
Abstract
With the successful launch of the IKONOS satellite, very high resolution imagery (up to 1-m resolution) is within the reach of civilian users. In the one-meter spatial resolution images acquired by the IKONOS satellite, details of buildings, shadows, roads, vehicles, individual trees, and even aggregates of people are visible. The visibility of such details opens up many new applications which require the use of contextual and geometrical information contained in the images. In this paper, we present some examples where geometrical and contextual information are used. These examples include estimating building height from its shadow, automatic delineation of tree crowns for enumerating trees in oil palm plantations, and extraction of linear features for cartographic applications.
1. Introduction
Very high resolution imagery (up to 1-m resolution) is within the reach of civilian users with the successful launch of the IKONOS-2 satellite in September 24, 1999. The IKONOS satellite provides imagery at two resolution modes: a 1-m panchromatic mode and a 4-m multispectral mode with four (red, green, blue and near infrared) spectral bands. Images of a same area can be acquired simultaneously in the two resolution modes and merged to form a pan-sharpened natural colour image at 1-m resolution.
Many details such as building structures, roads, vehicles, individual tree crowns, and even aggregates of people can be seen clearly in the very-high resolution imagery. Pixel-based methods of image analysis will not work successfully in such imagery. In order to fully exploit the information contained in the imagery, image processing and analysis algorithms utilising the textural, contextual and geometrical properties are required. Such algorithms make use of the relationship between neighbouring pixels for information extraction. Incorporation of a-priori information is sometimes required. A multi-resolutional approach is also a useful strategy when dealing with very high resolution imagery. In this case, pixel-based method can be used in the lower resolution mode and merged with the contextual and textural method at higher resolutions.
In this paper, we examine three examples where contextual and geometrical information are used in extracting information from 1-m resolution IKONOS images: automatic delineation and counting of tree crowns in an oil palm plantation, road extraction in an urban area and estimating building height from shadow.
2. Tree Counting in Oil Palm Plantations
Oil palm plantation owners have a genuine interest in knowing the number of trees in their plantations for the fact that they need to monitor the production and to assess the value of the plantations. In the buying and selling of plantations, the interested parties evaluate a plantation not only by its size, but also by the quantity of palms within because knowing the actual number of trees will give them a better assessment of the production rate.
Oil palm plantations range in size from below 40 hectares to as large as 75,000 hectares. The current practice of tree census is to deploy workers into the plantations and count the palm trees manually. The cost of manual counting can be low but the accuracy is doubtful due to various human factors. Though going physically into a plantation to count trees is not a drudgery, it is extremely tedious and hard to verify. The availability of 1-meter resolution IKONOS satellite imagery provides a better alternative. The advantages are both in the bird-eye views it offers and in the convenience of working with digital imagery.
Figure 1 (Left) the appearance of oil palm trees in an IKONOS image,
(right) the image after smoothing
The resolution of the IKONOS images is high enough for individual trees to be clearly spotted and enumerated. In these images of young plantations, palm trees generally have dome-shaped intensity profiles after some smoothing. Young trees tend to be well delineated at this resolution because of their distances of separation. As the trees grow bigger, the gaps among individual trees decrease and eventually disappear. However, the dome-shaped profiles remain visible. See Figure 1(a) for a palm tree image and Figure 1(b) for the image after smoothing.
Counting trees in an image is much more manageable as workers can label the trees easily on the image. Verification is much easier. To further reduce the tediousness of the task and to speed it up, tree counting can actually be automated and the human worker needs only to verify the counting and make minor corrections when necessary. There are existing image processing techniques, which can be easily adopted and modified to automate tree counting. One technique which shows excellent results is that adopted from Brandtberg and Walter (1998). This technique is based on the concepts of edge and curvature in differential geometry. The boundaries of the dome-shaped intensity profiles show up as edge segments. The curvature primal sketch is then used to integrate (1) the information from the edge segments detected at a particular resolution, and, (2) the information obtained at various resolutions. The curvature primal sketches obtained at various resolutions are summed together to form a cumulative primal sketch. Peaks in the cumulative primal sketch are filtered using a local maximum filter of the size of a palm tree. Those peaks that pass the filtering are considered to represent trees. The shortcoming of this technique is that it uses only the gradient of intensity and ignores the magnitude of intensity itself. Sometimes, the magnitude of intensity can be a useful piece of information. For instance, tracks and lanes in plantations tend to appear very bright in the image. Such areas can be filtered away by thresholding.
