Insectos bibliófagos y roedores .1 Insectos

Successful road extraction in suburban areas often applies region-based methods to high spatial resolution data (Grote et al. 2012). For many studies, identification of road clusters is the primary research goal; however, it is often a necessary preprocessing step before per- forming vectorization, particularly in complex urban scenes, and may also be a component of a broader land cover classification. The characterization of methods within this section is somewhat arbitrary and includes division based on the processing framework as well as the algorithms used. It should be noted that many studies use multiple algorithms, for example, using one approach to create a binary layer that identifies road candidates, and then using another method to clean up errors.

9.3.1.1 Classification Framework

LiDAR analysis may be based on points within the LiDAR cloud or based on raster sur- faces that are derived from the LiDAR points—for example, bare earth, intensity, and first return height. These points or pixels may be treated individually or as homogeneous groups using object-based approaches. This section summarizes some of the studies that use each of these fundamental frameworks to identify roads using LiDAR data.

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9.3.1.1.1 Point- and Pixel-Based Classification

Like pixels in passive data sources, both LiDAR point clouds and pixels within LiDAR- derived surfaces can be classified to distinguish fundamental land cover types, such as buildings, roads, trees, and other urban features (Alharthy and Bethel 2003). Working at the point and pixel level, many studies use hierarchical classification techniques to extract roads from LiDAR data (Alharthy and Bethel 2003; Hu 2003; Clode et al. 2004). This may include preprocessing steps, such as high-pass filters to enhance road edges prior to extraction (Gecen and Sarp 2008), or selective application of various LiDAR components.

Alharthy and Bethel (2003) first used the intensity information of LiDAR data to detect roads based on their surface reflectivity, and then used inferences from the LiDAR eleva- tion information to filter out misclassified pixels that did not belong to the road category.

Hu (2003) and Clode et al. (2004) performed a similar classification in the reverse direc- tion, using the elevation and then the intensity information in a hierarchical framework.

Samadzadegan et al. (2009) used a classifier fusion approach to extract roads from LiDAR data using both the intensity and the range information. They generated raster layers with 1 m pixels from the intensity and range information, including first and last return data, and then explored two methods for fusing maximum likelihood and minimum distance classifiers: weighted majority voting and selected naïve bays. Samadzadegan et al. (2009) found that both fusion methods produced better results than the single classifiers, with selected naïve bays producing the best overall result.

Jiangui and Guang (2011) used elevation and intensity information to identify roads in airborne LiDAR data by first classifying the LiDAR point cloud into ground and non- ground points, and then using the LiDAR intensity to classify the ground points as either road or nonroad. Rottensteiner and Clode (2008) also directly classified the LiDAR points.

They used a rule-based approach based on assumptions related to height—that is, roads are generally on or near the digital surface model—and intensity—that is, roads tend to appear as dark features—to identify potential road points within the LiDAR cloud.

Rottensteiner and Clode (2008) also used the continuous nature of roads—that is, assum- ing that road points are typically surrounded by other road points—to use a local point density to filter out isolated pixels that may have met the height and intensity thresholds.

9.3.1.1.2 Object-Based Approaches

Increased access to high spatial resolution data sources—from both passive and active sensors—has stimulated a swell in object-based analysis for road extraction. Tiwari et al. (2009) used a multiresolution segmentation approach to include spectral, shape, context, and texture features in identifying roads in an urban environment. Tiwari et al.

(2009) performed an initial segmentation based on QuickBird imagery, and then used a LiDAR point cloud to separate ground and elevated roads. Chen et al. (2009) also used a hierarchical object-based approach to extract roads from QuickBird and LiDAR data sources. Chen et al. (2009) used a variety of object characteristics to decrease confusion, for example, roads were separated from spectrally similar classes using object compact- ness measures. Another common challenge in road extraction studies that Chen et al.

(2009) sought to solve through the object-based analysis was bridge identification. They used object symmetry measures to separate bridges from other tall, impervious objects, such as buildings. Im et al. (2008) used an object-based approach to classify land cover in an urban setting using decision trees based only on high-posting-density LiDAR data.

They evaluated shape and texture-based metrics but found object characteristics based on height and intensity produced the best classification results. Zhu and Mordohai (2009) used object-based analysis after first creating an initial ground plane mask using LiDAR

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height data. They then used image segmentation of the masked LiDAR intensity data to create both boundary and interior features of image objects to support road classification using a minimum cover approach.

9.3.1.2 Algorithms Used for Road Identification

Road clusters can be defined using a variety of image processing procedures (Clode et al.

2007) working from both point or pixel and object-based frames of reference. Mena (2003) summarizes many techniques used to identify roads from a variety of data sources. This section extends Mena’s work by exploring methods presented in recent studies, particu- lary those that incorporate LiDAR components.

9.3.1.2.1 Clustering and Calibration-Based Methods

Numerous studies have explored the utility of partition and grid-based clustering methods for road extraction. Gong et al. (2010) applied a k-means clustering approach to extract roads from LiDAR intensity data. Their two-class study—roads versus vegetation—showed improved results when LiDAR data were fused with multispectral imagery. Zhang (2006) also used a simple k-means clustering algorithm for performing image segmentation, and then used a fuzzy logic classifier to automatically identify road clusters from the clustering results. Agouris et al. (2001) first applied an unsupervised classification, and then deter- mined roads from the defined classes through a k-medians algorithm. Choi et al. (2008) used a clustering algorithm to automatically extract road networks from 3D LiDAR data in an urban environment. The clustering implemented by Choi et al. (2008) integrated height, reflectance, and geometric information to extract road points and used the shape and size of point clusters to remove nonroad features such as cars and trees. In addition to statisti- cal approaches, there is also a range of clustering techniques used for spatial analysis—

such as genetic algorithms—that have been inspired by natural systems. Saeedi et al. (2009) extracted urban features from LiDAR data using one such clustering algorithm, which was based around the foraging habits of a colony of honeybees.

Zuo and Quackenbush (2010) considered two components in delineating potential raster road strips: (1) identifying road-level features and (2) selecting impervious features. The first component was based around the commonly used assumption that roads generally lie on or near the bare earth surface, with the exception of elevated roads, bridges, and tun- nels. Zuo and Quackenbush (2010) sought to establish an optimal height threshold to select all the road-level features and remove all features with nonzero height (e.g., buildings and trees) and an optimum intensity for detecting impervious features. While thresholds can be established manually, Zuo and Quackenbush (2010) applied an automatic calibration model (Im et al. 2007) to delineate potential road strips. The automatic calibration model uses an exhaustive search technique with a stratified sampling design of the research space to determine optimal thresholds for each input layer. Zuo and Quackenbush (2010) used this automatic calibration model to find optimal thresholds for LiDAR-derived height and intensity to identify a preliminary raster road network.

9.3.1.2.2 Machine-Learning Approaches

Researchers have explored machine-learning algorithms for road extraction within both object- and pixel-based frameworks. Im et al. (2008) used decision trees to classify high- posting-density LiDAR data. Huang and Zhang (2009) used support vector machines (SVMs) to identify roads from multiscale object features. The SVM approach seeks to identify hyper- planes in the feature space to best separate the desired classes by maximizing the distance

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of the training point nearest to the decision boundary (Rottensteiner 2010). Zhan and Yu (2011) also applied SVMs to classify objects within a LiDAR point cloud. Jin (2012) compared a random forest (RF) approach to SVMs and the more traditional maximum likelihood clas- sifier to categorize land cover classes, including roads, in an urban environment. RFs com- bine multiple decision trees that are based on a random vector sampled independently and with the same distribution for all trees (Rottensteiner 2010). Jin (2012) integrated LiDAR data with optical imagery and found the RF-based feature selection and classification approach attained the best results in a pixel-based classification. The research presented by Carlson and Danner (2010) focused on bridge extraction to support hydrologic studies using the high- resolution digital elevation models (DEMs) that LiDAR can produce. Carlson and Danner (2010) used an adaptive boosting machine learning approach to identify bridges. The adap- tive boosting approach systematically combines sets of classifiers, which may individually have high error, to form a single classifier with low overall error (Carlson and Danner 2010).

9.3.1.2.3 Template-Based Methods

Researchers have also used template-based approaches to perform road extraction.

Although some studies (Lin et al. 2011) have used template-based methods to extract roads from very high-resolution imagery, other researchers have explored the benefit of tem- plates in working with LiDAR datasets (Zhao and You 2012). After creating a ground plane dataset, Zhao and You (2012) used elongated structure templates to fit local intensity dis- tributions to identify road candidates within LiDAR data. Zhao and You (2012) allowed the template center to shift within the road, to find the best local fit.

9.3.1.2.4 Mathematical Morphology

Mathematical morphology has proven useful in linear feature extraction (Quackenbush 2004) and has been successfully applied by many researchers in road extraction. Since mathematical morphology methods are useful for segmentation and image enhancement, such approaches are commonly applied to enhancing road network extraction from a variety of data sources.

Zhang et al. (1999) performed mathematical morphology to mitigate errors from objects that had spectral characteristics similar to road surfaces. Amini et al. (2002) found that mathemati- cal morphology operations simplified and eliminated errors in the image while maintaining shape characteristics. Chanussot et al. (1999) applied a mathematical morphological approach with fuzzy fusion techniques to extract roads from SAR satellite data.

The core operators in mathematical morphology are dilation and erosion. Dilation is used for expanding features in an image and closing any gaps, while erosion shrinks image fea- tures and eliminates small features. Additional operators, such as closing and opening, build from these fundamentals; the closing operation is dilation followed by erosion, and opening is erosion followed by dilation. Like many researchers, Zuo and Quackenbush (2010) also used multiple morphology operations. They applied morphological closing twice to a binary road image using small structural elements, initially an element with a three-pixel radius and then again with an element with a two-pixel radius, to remove small gaps in road strips and connect neighboring road pixels. Zuo and Quackenbush (2010) also applied morphological opening with a one-pixel radius to remove small or narrow clusters without affecting large ones. Wan et al. (2009) also used a variety of morphological tools to extract road networks from multispectral imagery. After applying a minimum distance classifier to create a binary road candidate image, Wan et al. (2009) used morphological opening to remove isolated ele- ments and also applied a skeleton extraction algorithm to obtain the centerline of the road network. Jin et al. (2012) also used morphological closing and opening to remove small holes and noise and to eliminate small pathways from their road network.

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