With the recent availability of commercial high resolution remote sensing panchromatic imagery from sensors such as
IKONOS and QUICKBIRD, it is possible to extract individual objects such as buildings from the imagery. However,
traditional extraction methods cannot get the desired accuracy, because knowledge is not utilized. In this paper, we put
forward a texture-based approach to get building information from the panchromatic imagery. Firstly, the image is
segmented based on texture of variogram feature. Building corner structure knowledge is also combined to detect and
connect building edges. Then we fill interiors of buildings through seed filling algorithm. In the final stage, point noises
and linear noises are eliminated from the imagery through area or shape index value. The accuracy assessment adopted
in this paper is GIS overlay analysis, which shows that 93.9% of building information is extracted correctly. The result
indicates that the approach supplies another new technique for interpreting high spatial resolution remotely sensed
imagery.
Delaunay triangulation is always used to construct TIN, and is also widely applied in manifold fields, for it can avoid
long and skinny triangles resulting in a nice looking map. A wide variety of algorithms have been proposed to construct
delaunay triangulation, such as divide-and-conquer, incremental insertion, trangulation growth, and so on. The
compound algorithm is also researched to construct delaunay triangulation, and prevalently it is mainly based on divide-and-conquer and incremental insertion algorithms. This paper simply reviews and assesses sweepline and divide-and-conquer
algorithms, based on which a new compound algorithm is provided after studying the sweepline algorithm
seriously. To start with, this new compound algorithm divides a set of points into several grid tiles with different
dividing methods by divide-and-conquer algorithm, and then constructs subnet in each grid tile by sweepline algorithm.
Finally these subnets are recursively merged into a whole delaunay triangulation with a simplified efficient LOP
algorithm. Because topological structure is important to temporal and spatial efficiency of this algorithm, we only store
data about vertex and triangle, thus edge is impliedly expressed by two adjacent triangles. In order to fit two subnets
merging better, we optimize some data structure of sweepline algorithm. For instance, frontline and baseline of
triangulation are integrated into one line, and four pointers point to where maximum and minimum of x axis and y axis
are in this outline. The test shows that this new compound algorithm has better efficiency, stability and robustness than
divide-and-conquer and sweepline algorithms. Especially if we find the right dividing method reply to different
circumstance,its superiority is remarkable.
With the recent availability of commercial high resolution remote sensing multispectral imagery from sensors such as
IKONOS and QuickBird, we can't get the accuracy of land-cover classification expected using pixel-based approach. In
this paper, we bring about object-based approach combined with the nearest neighbor to classify the QuickBird image of
LianYungang city. Firstly, the image is segmented into object feature, we make the shape feature and contextual relation
feature join the new feature space which is used to classify. And then we compare the classification of object-based
approach accuracy with the nearest neighbor method of classification result, we can draw a conclusion that the method of
classification in this paper can recognize geo-types much better. And the overall accuracy is 92.19%; the coefficient of
Kappa is 0.8835. Salt and pepper effect is decreased effectively. The result indicates that the approach of land-cover
classification combined object features with the nearest neighbor approach supplies another new technique for interpreting
high resolution remote sensed imagery.
The east Taihu lake region is characterized by high-density and large areas of enclosure culture area which tend to cause
eutrophication of the lake and worsen the quality of its water. This paper takes an area (380×380) of the east Taihu Lake
from image as an example and discusses the extraction method of combing texture feature of high resolution image with
spectrum information. Firstly, we choose the best combination bands of 1, 3, 4 according to the principles of the
maximal entropy combination and OIF index. After applying algorithm of different bands and principal component
analysis (PCA) transformation, we realize dimensional reduction and data compression. Subsequently, textures of the
first principal component image are analyzed using Gray Level Co-occurrence Matrices (GLCM) getting statistic Eigen
values of contrast, entropy and mean. The mean Eigen value is fixed as an optimal index and a appropriate conditional
thresholds of extraction are determined. Finally, decision trees are established realizing the extraction of enclosure
culture area. Combining the spectrum information with the spatial texture feature, we obtain a satisfied extracted result
and provide a technical reference for a wide-spread survey of the enclosure culture area.
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