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首页> 外文会议>Pixels, Objects, Intelligence: GEOgraphic Object Based Image Analysis for the 21st Century >BUILDING DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGERY USING ADAPTIVE FUZZY-GENETIC APPROACH
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BUILDING DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGERY USING ADAPTIVE FUZZY-GENETIC APPROACH

机译：使用自适应模糊遗传方法从高分辨率卫星图像中建立检测

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We present a technique for extracting the buildings from high resolution satellite imagery using adaptive fuzzy-genetic approach. The technique was inspired from the genetic image exploitation system, GENIE PRO, conducted by Perkins et al., (2005) but brings an important novelty, which is an adaptive-fuzzy module that fine-tunes the genetic algorithm parameters aiming to improve the feature extraction performance. The technique integrates the well known genetic algorithm concepts such as population, chromosome, gene, crossover and mutation into the fundamental image processing concepts. The population is defined as the set of chromosomes, which consists of a predetermined number of image processing operations (genes). The genes are comprised the basic image processing operations. The algorithm is initiated by selecting the training samples for the building and non-building areas from the imagery. The image processing operations are applied in a chromosome-by-chromosome basis to obtain specific attribute planes. These planes are then fed into Fisher Linear Discriminant (FLD) module, which finds an optimal discriminating hyper plane between the building and non-building features. Next, for each chromosome, the fitness values are calculated by analyzing the detection and mis-detection rates. After that the crossover and mutation operations are applied to arbitrary chromosome(s) to create a better population in the next generation by diversifying the current population. At the end of each generation cycle, the crossover and the mutation probabilities are adjusted by the adaptive-fuzzy module for the next generation. The evolutionary process is repeated until a satisfactory level of iteration is reached. Finally, a post-processing operation is performed in order to enhance the extracted building polygons by means of the morphological image processing operations. The approach was implemented on a selected urban area of the city of Ankara, Turkey using the 1-m resolution pan-sharpened IKONOS imagery. The study was found to be quite promising since the building regions were successfully extracted with an approximate detection rate of 90percent.
机译：我们使用自适应模糊遗传方法提出一种从高分辨率卫星图像中提取建筑物的技术。该技术受到Perkins等人的遗传图像剥削系统Genie Pro的启发，（2005），但是带来了一个重要的新颖性，这是一种自适应模糊模块，其微调旨在改善特征的遗传算法参数提取性能。该技术将众所周知的遗传算法概念纳入群体，染色体，基因，交叉和突变中的基本图像处理概念。人口被定义为染色体集，其包括预定数量的图像处理操作（基因）。基因包括基本图像处理操作。通过选择来自图像的建筑物和非建筑区域的训练样本来启动该算法。图像处理操作以染色体染色体施加，以获得特定的属性平面。然后将这些平面送入Fisher线性判别（FLD）模块，该模块在建筑物和非建筑功能之间找到最佳区分的超平面。接下来，对于每种染色体，通过分析检测和误报率来计算适应值。之后，将交叉和突变操作应用于任意染色体，以通过多样化目前的人群来在下一代创造更好的人口。在每个一代循环结束时，通过用于下一代的自适应模糊模块调整交叉和突变概率。重复进化过程，直到达到令人满意的迭代水平。最后，执行后处理操作，以通过形态学图像处理操作来增强提取的建筑多边形。该方法是在土耳其Ankara市的选定城区实施，使用了1米分辨率泛尖的Ikonos Imagery。由于建筑物区域成功提取了90平方的近似检测率，因此发现该研究非常有前途。

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