Very High Resolution Images Research Articles

A template-matching based approach for extraction of roads from very high-resolution remotely sensed imagery

Automated or semi-automated extraction of road networks is a prerequisite to fast acquisition and update of geospatial data. Actually, line-shaped lane markings and/or median strips on road surfaces are less impacted by occlusions of vehicles or shadows of trees than the other parts of road surfaces on very high-resolution (VHR) remotely sensed imagery. These features provide a clue for road extraction. This article proposes an approach for semi-automated extraction of road networks by tracking apparent lane markings and/or median strips on VHR imagery. After preprocessing the raw image, three seed points on a short road segment are manually selected, which indicate starting point, direction and width of the road, respectively. Based on the manually selected data, a reference template of the road, which is composed of two components: a cross-section profile, rectangular templates of lane markings and median strips, is created. With the created reference template, automated road tracking is triggered. During the process of road tracking, a least-squares template matching is employed to search the optimal road centreline points, and a human operator is retained in the loop to guide the computer. The above operation is repeated until an entire road network is completely extracted. Tests of the above-proposed method are conducted on both aerial and VHR satellite imagery. The results show that the proposed method can successfully track over 94% of the highways and 81% of the arterial roads from the VHR images, and save the time of 26% when comparing to traditional methods.

A Novel Method for Urban Road Damage Detection Using Very High Resolution Satellite Imagery and Road Map

In this paper, a novel method for detecting earthquake damage to urban road networks using very high resolution (VHR) satellite images is proposed. The study focuses on road blockage caused by rubble from collapsed buildings. The road regions were first extracted from a pre-event road map. The damage to the road network was then detected from the post-event VHR image of the road regions using object-based classification, where both spectral and texture information was used. The damage to adjacent buildings was also extracted and used to refine the initial result of the road damage detection. The proposed method was evaluated using bitemporal VHR images and the road map of Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. The experimental results showed that the proposed method outperformed the direct use of various data combinations in road damage detection and achieved the best performance, with an overall accuracy (OA) of 81.50 percent and a Kappa coefficient of 61.23 percent. In particular, when the refinement step with building damage information in the area adjacent to the road was applied, the OA and Kappa were significantly improved.

Semi-automatic road tracking by template matching and distance transformation in urban areas

Road surfaces are seriously disturbed by a variety of types of noise on very high resolution (VHR) remotely sensed imagery in urban areas, which is a tough challenge for most road tracking methods. In fact, the sizes of most types of noise such as vehicles, zebra crossings and lane markings are smaller than the width of road surfaces on VHR images. In this sense, some filtering techniques are helpful to weaken the negative effects of these types of image noise. In this article, a semi-automatic method is proposed to track roads on VHR imagery. Once a human operator inputs a seed point on a selected road, the spoke wheel algorithm is used to obtain road direction, road width and starting point, and generate a reference template. Then the automatic tracking is triggered. During the process, a reference template is moved to generate several target templates. For each target template, the differences of grey values between the reference template and the target template are calculated, and then thresholding is used to create a binary template. Subsequently, Euclidean distance transformation (EDT) is used to erode the small disturbing noises on each binary template, which is helpful to search optimal road centreline points. The above tracking process is repeated until a whole road network is completed. A QuickBird image covering Tai'an area is used for the test. The results demonstrate that our improved method can extract urban road networks more reliably, more robustly and faster than applying the least-squares template matching method.

The efficiency of sampling very high resolution images for area estimation in the European Union

Very high resolution (VHR) images are a valuable information source to estimate land cover area and land cover change. When full coverage of a region with VHR images is not affordable, a sample of images can be considered. Square grids provide a practical sampling frame for VHR images. When using a land cover map as pseudo-truth, the sampling variance is easily assessed but may be overestimated if the land cover map has a coarse resolution. To estimate the potential sampling variance of a cluster sampling scheme, we propose a method based on intra-cluster correlation (ICC) computed from a correlogram. The ‘equivalent number of points’ is a useful indicator to quantify cost-efficiency of sites of a given size. We obtained poor efficiency results for area estimation of major land cover types in the European Union (EU) with a sample of 10 km × 10 km sites, but results are more encouraging for classes with a more scattered layout or for land cover change.

Semi-automatic extraction of road networks by least squares interlaced template matching in urban areas

Semi-automatic extraction of roads is greatly needed to accelerate the acquisition and updating of geodata. In fact, most roads are often seriously impacted by various types of noise, such as the occlusion of vehicles and the shadow of trees on very-high-resolution (VHR) remotely sensed imagery, which makes most of the existing road trackers ineffective. Fortunately, lane markings are less frequently disturbed than other parts of the road surface, and they provide a unique clue for road tracking on VHR images. In this paper, a semi-automatic method is proposed to extract roads with lane markings in urban areas. First, an operator detects a road segment and selects three seed points, which indicate the starting point, the direction and the width of a road, and lane markings near the seed points are automatically detected. Subsequently, an interlaced reference template of the selected road, composed of a few profiles of the road surface and the detected rectangular templates of lane markings on the road surface, is constructed. The reference template is then convolved with the image, and least squares template matching is employed to track the road axis. To complete the task, the process is then repeated. Various types of images are used for test, and the results show that our method is capable of robustly extracting roads from VHR imagery because the special configuration of the reference template can decrease side effects such as the occlusion of vehicles and the shadow of trees as much as possible.

Automatic segmentation of VHR images using type information of local structures acquired by mathematical morphology

The morphological profile (MP) and differential morphological profile (DMP) have been used extensively to acquire spatial information to be used in the segmentation of very high resolution (VHR) remotely sensed images. In most of the previous approaches, the maxima of the MP and DMP were investigated to estimate the best representative scale in the spatial domain for the pixel under consideration. Then, the object type (i.e. dark, bright or flat) was estimated based on the location of the maximum. Finally, the image segmentation was performed using the scale and type information as features. This approach usually causes over-segmentation. In this study, we also investigate the relevance of the DMP and the meaningful object types underlying the pixel of interest, however, instead of the maxima of the DMP, the type information is estimated using the whole DMP which is weighted by a weight function. Thus, the scale is not estimated directly but used indirectly in the estimation of the characteristic type for the object to which the pixel belongs. Then, the pixels are clustered based on their types only. The method has been applied to panchromatic high resolution QuickBird satellite images of the city of Ankara, Turkey. The results of the method were compared with previous studies and the proposed method seems to segment the images more precisely and semantically than the previous approaches.

Co-seismic surface effects from very high resolution panchromatic images: the case of the 2005 Kashmir (Pakistan) earthquake

Abstract. The use of Very High Resolution (VHR) satellite panchromatic image is nowadays an effective tool to detect and investigate surface effects of natural disasters. We specifically examined the capabilities of VHR images to analyse earthquake features and detect changes based on the combination of visual inspection and automatic classification tools. In particular, we have used Quickbird (0.6 m spatial resolution) images for detecting the three main co-seismic surface features: damages, ruptures and landslides. The present approach has been applied to the 8 October 2005, Mw7.6 Kashmir, Pakistan, earthquake. We have focused our study in and around the main urban areas hit by the above earthquake specifically at Muzaffarabad and Balakot towns. The automatic classification techniques provided the best results wherever dealing with the damage to man-made structures and landslides. On the other hand, the visual inspection method demonstrated in addressing the identification of rupture traces and associated features. The synoptic view (concerning landslide, more than 190 millions of pixels have been automatically classified), the spatiotemporal sampling and the fast automatic damage detection using satellite images provided a reliable contribution to the prompt response during natural disaster and for the evaluation of seismic hazard as well.

A Multilevel Hierarchical Image Segmentation Method for Urban Impervious Surface Mapping Using Very High Resolution Imagery

This paper presents a hierarchical image segmentation method that combines multichannel watershed transformation and dynamics of watershed contours for the segmentation of very high resolution (VHR) multispectral imagery. The image gradient was first extracted from a multispectral image using a multichannel morphological method, followed by classical watershed transformation to produce an initial segmentation result. The resulting watershed contours were then analyzed according to their relevance relative to the minima of the adjacent basins to construct an image containing information about their dynamics. By thresholding the image of the contour dynamics at different levels, multilevel hierarchical segmentation results with different levels of detail were achieved. The proposed method was evaluated by comparing with existing methods through visual inspection, quantitative measures and applications in urban impervious surface mapping, using two sets of VHR image data. The experimental results showed that the proposed method produced more accurate segmentation results compared to an existing single-level segmentation method, in terms of visual and quantitative evaluations. While used for urban impervious surface mapping, the proposed method achieved an overall accuracy significantly higher than the pixel based classification method, and also higher than the object based classification using a single-level segmentation result. Compared with the most widely used segmentation method implemented in the eCognition, the proposed method achieved a comparable performance, although they have different segmentation details. The proposed segmentation method can be used in relevant VHR image processing and applications.

A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

Detecting buildings from very high resolution (VHR) aerial and satellite images is extremely useful in map making, urban planning, and land use analysis. Although it is possible to manually locate buildings from these VHR images, this operation may not be robust and fast. Therefore, automated systems to detect buildings from VHR aerial and satellite images are needed. Unfortunately, such systems must cope with major problems. First, buildings have diverse characteristics, and their appearance (illumination, viewing angle, etc.) is uncontrolled in these images. Second, buildings in urban areas are generally dense and complex. It is hard to detect separate buildings from them. To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework. We first introduce four different local feature vector extraction methods. Extracted local feature vectors serve as observations of the probability density function (pdf) to be estimated. Using a variable-kernel density estimation method, we estimate the corresponding pdf. In other words, we represent building locations (to be detected) in the image as joint random variables and estimate their pdf. Using the modes of the estimated density, as well as other probabilistic properties, we detect building locations in the image. We also introduce data and decision fusion methods based on our probabilistic framework to detect building locations. We pick certain crops of VHR panchromatic aerial and Ikonos satellite images to test our method. We assume that these crops are detected using our previous urban region detection method. Our test images are acquired by two different sensors, and they have different spatial resolutions. Also, buildings in these images have diverse characteristics. Therefore, we can test our methods on a diverse data set. Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos satellite and our aerial images.

Semisupervised Feature Selection for Unbalanced Sample Sets of VHR Images

A semisupervised feature selection method, named asymmetrically local discriminant selection (ALDS), is proposed to evaluate the class separability of unbalanced sample sets from very high resolution (VHR) imagery in an object-oriented classification. In order to cope with class imbalance, ALDS incorporates asymmetric misclassification costs of classes into weight matrices. Furthermore, this method locally exploits multiple kinds of relationships between sample pairs to more accurately assess the ability of features in preserving the geometrical and discriminant structures. The experimental results on VHR satellite and airborne imagery attest to the effectiveness and practicability of ALDS.

Operational Performance of an Automatic Preliminary Spectral Rule-Based Decision-Tree Classifier of Spaceborne Very High Resolution Optical Images

In the last 20 years, the number of spaceborne very high resolution (VHR) optical imaging sensors and the use of satellite VHR optical images have continued to increase both in terms of quantity and quality of data. This has driven the need for automating quantitative analysis of spaceborne VHR optical imagery. Unfortunately, existing remote sensing image understanding systems (RS-IUSs) score poorly in operational contexts. In recent years, to overcome operational drawbacks of existing RS-IUSs, an original two-stage stratified hierarchical RS-IUS architecture has been proposed by Shackelford and Davis. More recently, an operational automatic pixel-based near-real-time four-band IKONOS-like spectral rule-based decision-tree classifier (ISRC) has been downscaled from an original seven-band Landsat-like SRC (LSRC). The following is true for ISRC: (1) It is suitable for mapping spaceborne VHR optical imagery radiometrically calibrated into top-of-atmosphere or surface reflectance values, and (2) it is eligible for use as the pixel-based preliminary classification first stage of a Shackelford and Davis two-stage stratified hierarchical RS-IUS architecture. Given the ISRC “full degree” of automation, which cannot be surpassed, and ISRC computation time, which is near real time, this paper provides a quantitative assessment of ISRC accuracy and robustness to changes in the input data set consisting of 14 multisource spaceborne images of agricultural landscapes selected across the European Union. The collected experimental results show that, first, in a dichotomous vegetation/nonvegetation classification of four synthesized VHR images at regional scale, ISRC, in comparison with LSRC, provides a vegetation detection accuracy ranging from 76% to 97%, rising to about 99% if pixels featuring a low leaf area index are not considered in the comparison. Second, in the generation of a binary vegetation mask from ten panchromatic-sharpened QuickBird-2 and IKONOS-2 images, the operational performance measurement of ISRC is superior to that of an ordinary normalized difference vegetation index thresholding technique. Finally, the second-stage automatic stratified texture-based separation of low-texture annual cropland or herbaceous range land (land cover class AC/HR) from high-texture forest or woodland (land cover class F/W) is performed in the discrete, finite, and symbolic ISRC map domain in place of the ordinary continuous varying, subsymbolic, and multichannel texture feature domain. To conclude, this paper demonstrates that the automatic ISRC is eligible for use in operational VHR satellite-based measurement systems such as those envisaged under the ongoing Global Earth Observation System of Systems (GEOSS) and Global Monitoring for the Environment and Security (GMES) international programs.

Urban building damage detection from very high resolution imagery using OCSVM and spatial features

The availability of commercial very high resolution (VHR) satellite imagery makes it possible to detect and assess building damage in the aftermath of earthquake disasters using these data. Although conventional change detection methods may be used to assess the building damage, the analysis is directed to all classes, both damaged and undamaged, but is not focused on the class of interest. In this paper, we proposed to detect the building damage in urban environments from multitemporal VHR image data using the One-Class Support Vector Machine (OCSVM), a recently developed one-class classifier, which requires training samples from the building damage only. This was illustrated with building damage detection in an urban environment from multitemporal Quickbird images. The detection was conducted at pixel level and object level. Different input vectors for the OCSVM classifier were tested in order to assess the discrimination power of spectral and spatial features: pixel level spectral features and texture features, as well as object‐based features. The results showed that the OCSVM performed better on the object level, with an overall accuracy of 82.33% and a kappa coefficient of 60.09%. The results also showed that the OCSVM provides a useful framework that can combine different features and focus on the building damage class of interest. More spatial features are needed to be exploited to obtain more accurate detection results in future studies.

Comparison of Vector Stacking, Multi-SVMs Fuzzy Output, and Multi-SVMs Voting Methods for Multiscale VHR Urban Mapping

The objective of this letter is to integrate multiscale information for urban mapping using very high resolution (VHR) imagery. Three multiscale fusion methods were presented: 1) vector stacking (VS); 2) multiple support vector machines (multi-SVMs) fuzzy output; and 3) multi-SVMs voting. Two kinds of spatial features were used to obtain multiscale representations of VHR images: morphological structural features and object-based approaches. In experiments, the Reflective Optics System Imaging Spectrometer-03 Pavia Center and University, the Hyperspectral Digital Imagery Collection Experiment Washington DC Mall, and the Quickbird Beijing data sets were used for algorithm validation. The experimental results revealed that, in most cases, the VS fusion outperformed other methods because it was able to create a new high-dimensional multiscale feature space and enhance the class separability. It was also shown that the multi-SVMs fuzzy fusion could optimize and reorganize the multiscale information effectively. Furthermore, multi-SVMs fuzzy output was better than multi-SVMs voting because the former was able to exploit the probabilistic output, while the latter only considered the crisp classification label. In addition, it is suggested that VS fusion is suitable for morphological features; however, for the object-based classification, the multiscale fusion methods do not necessarily yield better results than the single-scale classification in terms of accuracies.

Analysis and Adaptive Estimation of the Registration Noise Distribution in Multitemporal VHR Images

This paper analyzes the problem of change detection in very high resolution (VHR) multitemporal images by studying the effects of residual misregistration [registration noise (RN)] between images acquired on the same geographical area at different times. In particular, according to an experimental analysis driven from a theoretical study, the main effects of RN on VHR images are identified and some important properties are derived and described in a polar framework for change vector analysis. In addition, a technique for an adaptive and unsupervised explicit estimation of the RN distribution in the polar domain is proposed. This technique derives the RN distribution according to both a multiscale analysis of the distribution of spectral change vectors and the Parzen windows method. Experimental results obtained on simulated and real multitemporal data sets confirm the validity of the proposed analysis, the reliability of the derived properties on RN, and the effectiveness of the proposed estimation technique. This technique represents a very promising tool for the definition of change-detection methods for VHR multitemporal images robust to RN.

Incorporating Generic and Specific Prior Knowledge in a Multiscale Phase Field Model for Road Extraction From VHR Images

This paper addresses the problem of updating digital road maps in dense urban areas by extracting the main road network from very high resolution (VHR) satellite images. Building on the work of Rochery <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">al.</i> (2005), we represent the road region as a ldquophase fieldrdquo. In order to overcome the difficulties due to the complexity of the information contained in VHR images, we propose a multiscale statistical data model. It enables the integration of segmentation results from coarse resolution, which furnishes a simplified representation of the data, and fine resolution, which provides accurate details. Moreover, an outdated GIS digital map is introduced into the model, providing <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">specific</i> prior knowledge of the road network. This new term balances the effect of the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generic</i> prior knowledge describing the geometric shape of road networks (i.e., elongated and of low-curvature) carried by a ldquophase field higher order active contourrdquo term. Promising results on QuickBird panchromatic images and comparisons with several other methods demonstrate the effectiveness of our approach.

The Influence of DEM Accuracy on Topographic Correction of Ikonos Satellite Images

There is no research which specifically investigates the influence of the Digital Elevation Model (DEM) on topographic correction of satellite images. Such an investigation is necessary in view of the very high resolution (VHR) of recent sensors such as Ikonos and QuickBird and the low availability of accurate height information for the derivation of slope and aspect values. A comparative study of multispectral Ikonos images is presented using eight selected interpolation techniques with contour data. The DEM influence was evaluated by analyzing the variance and classification accuracy of topographically corrected images using the different DEMS. These were found to vary widely, with a 30 percent reduction in variance and a 20 percent improvement in overall classification accuracy between the worst and best performing interpolation techniques. Furthermore, smoothing techniques commonly used for the removal of noise in interpolated contour data or in existing DEMs were found to offer no significant improvement in intra-class variance or classification accuracy, when used with a grid resolution compatible with VHR images. However, a more rigorous planar slope function was found to be more effective, and when combined with the best interpolator, the natural neighbor, or Sibson, method, very high classification accuracy was achieved in the topographically corrected images.

A walk down the Cap de Creus canyon, Northwestern Mediterranean Sea: Recent processes inferred from morphology and sediment bedforms

The Cap de Creus canyon, northwestern Mediterranean Sea, belongs to a complex network of submarine canyons cutting the western Gulf of Lion continental shelf and opening into the larger Sète canyon. Swath bathymetry data, MAK-1M deep-towed side-scan sonar imagery and 5 kHz high resolution seismic reflection profiles show striking morphologies in the Cap de Creus canyon floor and walls. As a consequence of the canyon head and the upper reach severe incision, the continental shelf dramatically narrows in front of the Creus Cape promontory. The upper canyon has a flat-bottomed thalweg incised in a mega-scale sediment furrow field displaying hyperbolic seismic facies. The tens of kilometres long linear furrows extend also over the middle canyon down to 1400 m of water depth. The furrows on either side of the canyon are not parallel but oblique and display varying degrees of excavation. Mid-channel sediment bars are locally present in the thalweg, which is made of sandy lag deposits, as revealed by its acoustic response and verified by sediment samples. The middle canyon is linear and steep, with an up to 700 m high southern wall, contrasting with the sinuous, smooth lower canyon, which is controlled by flowage of the underlying Messinian evaporites. Large sections of the canyon are affected by sediment instability processes. The lower Cap de Creus canyon hangs up to 260 m over its distalmost reach and the Sète canyon through a narrow, less than 1 km wide, gorge. Numerous scours up to 10 m deep suggesting bed load transport occupy the lower Sète canyon immediately downstream of the Cap de Creus canyon mouth. The data set provides the first complete very-high resolution imaging of a submarine canyon from its upper part down to its distalmost reach. The observations evidence a wide set of erosion, transport and deposition processes along the Cap de Creus canyon, including sediment entrapment at the canyon head, furrow-generating dense water cascading through the southern wall, along-channel currents strong enough to excavate specific sections of the channel floor and bed load sediment transport as demonstrated by the presence of mega-ripples, crescent scours and grooves in the lower canyon.

Panchromatic sharpening of remote sensing images using a multiscale Kalman filter

This paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with high-resolution panchromatic observations. The proposed method exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and up-sampling the wavelet filter bank, and the vector multiscale Kalman filter, which is used to model the injection process of wavelet details. Kalman modelization is exploited by spatial detail analysis at coarser scales in which multispectral and panchromatic representations are known. Results are presented and discussed on very-high resolution images acquired by Quickbird satellite systems. Fusion simulations on spatially degraded data and fusion tests at the full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method.

Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images

Predicting structural organization and biomass of tropical forest from remote sensing observation constitutes a great challenge. We assessed the potential of Fourier-based textural ordination (FOTO) to estimate mangrove forest biomass from very high resolution (VHR) IKONOS images. The FOTO method computes texture indices of canopy grain by performing a standardized principal component analysis (PCA) on the Fourier spectra obtained for image windows of adequate size. For two distinct study sites in French Guiana, FOTO indices derived from a 1 m panchromatic channel were able to consistently capture the whole gradient of canopy grain observed from the youngest to decaying stages of mangrove development, without requiring any intersite image correction. In addition, a multiple linear regression based on the three main textural indices yielded accurate predictions of mangrove total aboveground biomass. Since FOTO indices did not saturate for high biomass values, predictions were furthermore unbiased, even for levels above 450 t of dry matter per hectare. Maps of canopy texture (with RGB coding) and biomass were then produced over 8000 ha of unexplored, low accessibility mangrove. Applying the FOTO method to the 4 m near-infrared channel yielded acceptable results with some limitations for characterization of juvenile mangrove types. We finally discuss the influence of technical aspects pertaining to VHR images and to FOTO implementation (especially the size of the window used to compute Fourier spectra) and we evoke the interesting prospect of broad regional validity offered by the method to characterize high biomass tropical forest from standardized measures of canopy grain.

Galileo Observations of Europa's Opposition Effect

During Galileo's G7 orbit, the Solid State Imaging (SSI) camera acquired pictures of the spacecraft shadow point on Europa's surface as well as a comparison set of images showing the same geographic region at phase angle α = 5°. Coverage, obtained at three spectral bandpasses (VLT, 0.41 μm, GRN, 0.56 μm; and 1MC, 0.99 μm) at a spatial resolution of 404 m/pixel, shows a 162 × 220-km region of Europa's surface located at 30°N, 162°W. We have used these images to measure the near-opposition spectrophotometric behavior of four primary europan terrain materials: IR-bright icy material, IR-dark icy material, dark lineament material, and dark spot material. The high spatial resolution of the G7 images reveal low-albedo materials in dark spots that are among the darkest features (17% albedo at 0.56 μm and 5° phase) yet found on icy Galilean satellites. While material of comparable albedo is found on Ganymede and Callisto, low-albedo europan materials are much redder. All europan surface materials exhibit an opposition effect; however, the strength of the effect, as measured by the total increase in reflectance as phase angle decreases from α = 5° to α = 0°, varies among terrains. The opposition effects of IR-bright icy and IR-dark icy materials which dominate Europa's surface are about 1.5 times larger than predicted from pre-Galileo studies. Low-albedo materials in dark spots exhibit unusually intense opposition effects (up to four times larger than bright icy europan terrains), consistent with the presence of a strong shadow-hiding opposition surge. The strengths of the opposition surges among average europan terrains systematically vary with terrain albedo and can be explained in terms of the simultaneous contributions of shadow-hiding and coherent-backscatter to the total opposition effect. Coherent backscatter introduces a narrow angular contribution (<0.2° wide) to all europan terrains while the presence of a shadow-hiding contribution is revealed by the fact that the opposition surge is especially strong in low-albedo terrain materials. Stratigraphically young ridges with relatively high topographic relief exhibit anomalously weak opposition surges that could be caused by the presence of relatively coarse-grained regolith, highly compacted particulates, exposures of solid ice, or some combination of these. Very-high resolution images (22 m/pixel) of young-appearing ridges suggest that downslope wasting of particulate cover exposes more lithified ice along ridge crests and wall escarpments. We propose that over time, erosion of ridge topography accompanied by accumulation of detritus and mantling by regolith cover results in more mature ridge surfaces of increasingly lower (mature) porosity. We interpret the dark, reddish color of dark ridges and lineaments as a particulate coating of low-albedo materials vented from (or collected adjacent to) reactivated ridge-margins and within lenticulae.