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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >DETECTION OF CRESCENT SAND DUNES CONTOURS IN SATELLITE IMAGES USING AN ACTIVE SHAPE MODEL WITH A CASCADE CLASSIFIER
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DETECTION OF CRESCENT SAND DUNES CONTOURS IN SATELLITE IMAGES USING AN ACTIVE SHAPE MODEL WITH A CASCADE CLASSIFIER

机译：使用带分级分类器的主动形状​​模型检测卫星图像中的近期沙丘轮廓

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Crescent sand dunes called barchans are the fastest moving sand dunes in the desert, causing disturbance for infrastructure and threatening human settlements. Their study is of great interest for urban planners and geologists interested in desertification (Hugenholtz et al., 2012). In order to study them at a large scale, the use of remote sensing is necessary. Indeed, barchans can be part of barchan fields which can be composed of thousands of dunes (Elbelrhiti et al.2008). Our region of interest is located in the south of Morocco, near the city of Laayoune, where barchans are stretching over a 400?km corridor of sand dunes. We used image processing techniques based on machine learning approaches to detect both the location and the outlines of barchan dunes. The process we developed combined two main parts: The first one consists of the detection of crescent shaped dunes in satellite images using a supervised learning method and the second one is the mapping of barchans contours (windward, brink and leeward) defining their 2D pattern. For the detection, we started by image enhancement techniques using contrast adjustment by histogram equalization along with noise reduction filters. We then used a supervised learning method: We annotated the samples and trained a hierarchical cascade classifier that we tested with both Haar and LBP features (Viola et Jones, 2001; Liao et al., 2007). Then, we merged positive bounding boxes exceeding a defined overlapping ratio. The positive examples were then qualified to the second part of our approach, where the exact contours were mapped using an image processing algorithm: We trained an ASM (Active Shape Model) (Cootes et al., 1995) to recognize the contours of barchans. We started by selecting a sample with 100 barchan dunes with 30 landmarks (10 landmarks for each one of the 3 outlines). We then aligned the shapes using Procrustes analysis, before proceeding to reduce the dimensionality using PCA. Finally, we tested different descriptors for the profiles matching: HOG features were used to construct a multivariate Gaussian model, and then SURF descriptors were fed an SVM. The result was a recursive model that successfully mapped the contours of barchans dunes. We experimented with IKONOS high resolution satellite images. The use of IKONOS high resolution satellite images proved useful not only to have a good accuracy, but also allowed to map the contours of barchans sand dunes with a high precision. Overall, the execution time of the combined methods was very satisfying.
机译：新月形沙丘（barchans）是沙漠中移动速度最快的沙丘，对基础设施造成干扰，并威胁到人类住区。他们的研究引起了对沙漠化感兴趣的城市规划者和地质学家的极大兴趣（Hugenholtz等，2012）。为了大规模研究它们，必须使用遥感技术。确实，Barchans可能是Barchan领域的一部分，Barchan领域可以由数千个沙丘组成（Elbelrhiti等，2008）。我们感兴趣的地区位于摩洛哥南部，靠近拉尤恩市，那里的巴尔坎人正沿着一条绵延400千米的沙丘走廊延伸。我们使用基于机器学习方法的图像处理技术来检测Barchan沙丘的位置和轮廓。我们开发的过程包括两个主要部分：第一个部分包括使用监督学习方法检测卫星图像中的月牙形沙丘，第二个部分是定义其2D模式的barchans轮廓（迎风，边缘和背风）的映射。对于检测，我们从图像增强技术开始，该技术使用了通过直方图均衡化进行的对比度调整以及降噪滤波器。然后，我们使用了监督学习方法：我们对样本进行注释，并训练了我们使用Haar和LBP功能进行测试的分层级联分类器（Viola等，2001; Liao等，2007）。然后，我们合并了超出定义重叠率的正边界框。积极的例子然后被验证为我们方法的第二部分，其中使用图像处理算法来绘制精确的轮廓：我们训练了ASM（主动形状模型）（Cootes等，1995）来识别Barchans的轮廓。我们首先选择一个具有100个具有30个地标的沙丘的样本（三个轮廓中的每一个都具有10个地标）。然后，我们使用Procrustes分析对齐形状，然后继续使用PCA减少尺寸。最后，我们测试了用于轮廓匹配的不同描述符：HOG特征用于构建多元高斯模型，然后SURF描述符被提供给SVM。结果是一个递归模型，该模型成功地绘制了Barchans沙丘的轮廓。我们尝试了IKONOS高分辨率卫星图像。事实证明，使用IKONOS高分辨率卫星图像不仅具有较高的准确性，而且还可以高精度地绘制巴坎沙丘的轮廓。总体而言，组合方法的执行时间非常令人满意。

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