Object-based Features Research Articles

Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities

Over the last two decades (since ca. 2000), Geographic Object-Based Image Analysis (GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote-sensing imagery. During this time, research interests have demonstrated a shift from the development of GEOBIA theoretical foundations to advanced geo-object-based models and their implementation in a wide variety of real-world applications. We suggest that such a rapid GEOBIA evolution warrants the need for a systematic review that defines the recent developments in this field. Therefore, the main objective of this paper is to elucidate the emerging trends in GEOBIA and discuss potential opportunities for future development. The emerging trends were found in multiple subfields of GEOBIA, including data sources, image segmentation, object-based feature extraction, and geo-object-based modeling frameworks. It is our view that understanding the state-of-the-art in GEOBIA will further facilitate and support the study of geographic entities and phenomena at multiple scales with effective incorporation of semantics, informing high-quality project design, and improving geo-object-based model performance and results.

Unsupervised road extraction via a Gaussian mixture model with object-based features

ABSTRACTAutomatic road extraction from remotely sensed images is an important and challenging task. This article proposes an unsupervised road detection method based on a Gaussian mixture model and object-based features. Our approach has five major stages, i.e. superpixel segmentation, feature description, homogeneous region merging, clustering via the Gaussian mixture model, and outlier filtering. In the third step, we present a graph-based region merging algorithm, in which the nodes of the graph are superpixels and edges are the similarities of intensity, colour, and texture. We also define two shape features, called deviation of parallelism (DoP) and narrow rate (NR), to automatically recognize road layer and filter outliers in the last step. We evaluated the proposed method on a variety of datasets, in which the Vaihingen dataset from the International Society for Photogrammetry and Remote Sensing Test Project is also included. Results demonstrate the power of our approach compared with some state-of-the-art methods.

Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery

The developments in the use of unmanned aerial vehicles (UAVs) and advanced imaging sensors provide new opportunities for ultra-high resolution (e.g., less than a 10 cm ground sampling distance (GSD)) crop field monitoring and mapping in precision agriculture applications. In this study, we developed a strategy for inter- and intra-row weed detection in early season maize fields from aerial visual imagery. More specifically, the Hough transform algorithm (HT) was applied to the orthomosaicked images for inter-row weed detection. A semi-automatic Object-Based Image Analysis (OBIA) procedure was developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. Furthermore, the two binary weed masks generated from HT and OBIA were fused for accurate binary weed image. The developed RF classifier was evaluated by 5-fold cross validation, and it obtained an overall accuracy of 0.945, and Kappa value of 0.912. Finally, the relationship of detected weeds and their ground truth densities was quantified by a fitted linear model with a coefficient of determination of 0.895 and a root mean square error of 0.026. Besides, the importance of input features was evaluated, and it was found that the ratio of vegetation length and width was the most significant feature for the classification model. Overall, our approach can yield a satisfactory weed map, and we expect that the obtained accurate and timely weed map from UAV imagery will be applicable to realize site-specific weed management (SSWM) in early season crop fields for reducing spraying non-selective herbicides and costs.

Distinguishing the windthrow and hydrogeological effects of typhoon impact on agricultural lands: an integrative OBIA and PPGIS approach

ABSTRACTThe agricultural impacts of tropical cyclones remain the primary threat to livelihoods in Southeast Asia and Latin America. The impacts take two forms, one is windthrow, i.e. uprooted or snapped trees, caused by strong wind, and the other is hydrogeological effects from heavy precipitation. The empirical effects are different for the two forms. However, little previous research has been devoted to distinguishing the two effects to estimate agricultural losses, and even fewer have used moderate-resolution (10 m resolution) images. This study presents a methodological progression to address this deficiency. First, an object-based image analytical method distinguishes the two effects using Satellite Pour l’Observation de la Terre – 5 satellite images. Various object-based features are compared to acquire their spectral, textural, and geometric characteristics for the interpretation. Second, a public participation geographical information system (PPGIS) approach is developed that combines community empowerment and collaborative field survey to rebuild and represent ground truth during disasters for image validation. The method is applied to a case study of typhoon Bopha that struck Compostela Valley, eastern Mindanao, the Philippines in December 2012. Our assessment indicates that the producer accuracy reaches 88.9% for debris and mud flows and 83.3% for windthrow, and user accuracy reaches 94% and 81%, respectively. The result indicates that the proposed methods have great potential for distinguishing the two effects. It also highlights the efficacy of integrating PPGIS with remote sensing, for image validation purposes and in practice to enhance local residents’ environmental consciousness for enhancing adaptive capacity in resource limited regions.

Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations.

Recent studies have challenged the ventral/"what" and dorsal/"where" two-visual-processing-pathway view by showing the existence of "what" and "where" information in both pathways. Is the two-pathway distinction still valid? Here, we examined how goal-directed visual information processing may differentially impact visual representations in these two pathways. Using fMRI and multivariate pattern analysis, in three experiments on human participants (57% females), by manipulating whether color or shape was task-relevant and how they were conjoined, we examined shape-based object category decoding in occipitotemporal and parietal regions. We found that object category representations in all the regions examined were influenced by whether or not object shape was task-relevant. This task effect, however, tended to decrease as task-relevant and irrelevant features were more integrated, reflecting the well-known object-based feature encoding. Interestingly, task relevance played a relatively minor role in driving the representational structures of early visual and ventral object regions. They were driven predominantly by variations in object shapes. In contrast, the effect of task was much greater in dorsal than ventral regions, with object category and task relevance both contributing significantly to the representational structures of the dorsal regions. These results showed that, whereas visual representations in the ventral pathway are more invariant and reflect "what an object is," those in the dorsal pathway are more adaptive and reflect "what we do with it." Thus, despite the existence of "what" and "where" information in both visual processing pathways, the two pathways may still differ fundamentally in their roles in visual information representation.SIGNIFICANCE STATEMENT Visual information is thought to be processed in two distinctive pathways: the ventral pathway that processes "what" an object is and the dorsal pathway that processes "where" it is located. This view has been challenged by recent studies revealing the existence of "what" and "where" information in both pathways. Here, we found that goal-directed visual information processing differentially modulates shape-based object category representations in the two pathways. Whereas ventral representations are more invariant to the demand of the task, reflecting what an object is, dorsal representations are more adaptive, reflecting what we do with the object. Thus, despite the existence of "what" and "where" information in both pathways, visual representations may still differ fundamentally in the two pathways.

The application of discriminant analysis for mapping cereals and pasture using object-based features

ABSTRACTHigh mapping accuracies occur where crops differ spectrally (e.g.>90.0%; canola, corn, soybeans) and vice versa (e.g. <75.0%; cereals and pasture). Developing improved mapping methods has been an ongoing priority of Agriculture and Agri-Food Canada (AAFC) remote-sensing science. To this end, this study tests a data-driven object-based classification method using Discriminant Analysis (DA) method for mapping cereals and pasture from satellite data. In this approach, variables (number >400) derived from the image segmentation and object-based feature extraction of multi-date and multi-band optical (RapidEye) and microwave (RADARSAT-2) imagery were applied in a data-driven approach. We use in situ and satellite information collected over two study sites with different levels of heterogeneity (Winnipeg, Brandon) situated in the Canadian Prairies during the 2013 growing season to assess: (a) the type of DA model that most accurately classifies the cereals and pasture cover classes; and (b) how the classification accuracies obtained by the application of this DA model compare to those obtained from more traditional Maximum Likelihood (ML), Decision Tree (DT), and Random Forest (RF) classifications. We found that our DA-based approach was able to map cereals and pastures at our two study sites with the highest accuracies, but these accuracies did not improve significantly with the use of more complex DA model (including priori classification probabilities, more input principle components (PCs), the use of weights proportional to field area). Our results are encouraging for the wider application of the data-driven pre-processing of the inputs to the image classification by DA.

Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach

This paper proposes an efficient paddy field mapping method using object-based image analysis and a bitemporal data set acquired by Landsat-8 Operational Land Imager. In the proposed approach, image segmentation is the first step and its quality has a serious impact on the accuracy of paddy field classification. In order to improve segmentation quality, a new segmentation algorithm based on a frequently used method, fractal net evolution approach, is developed, with improvement mainly in merging criteria. In order to automate the process of scale parameter determination, an unsupervised scale selection method is utilized to determine the optimal scale parameter for the proposed image segmentation approach. After segmentation, four types of object-based features including geometric, spectral, textural, and contextual information are extracted and input into the subsequent classification procedure. By using a random forest classifier, paddy fields and nonpaddy fields are separated. The proposed image segmentation method and the final classification result are both quantitatively evaluated. Our segmentation method outperformed two popular algorithms according to three supervised evaluation criteria. The classification result with overall accuracy of 91.00% and kappa statistic of 0.82 validated the effectiveness of the proposed framework. Further analysis on feature importance indicated that spectral features made the most contribution as compared to the other three types of object-based features.

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

This paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI) and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely related in time scenes, one for each sensor, were classified by using Object Based Image Analysis and Random Forest (RF). The RF input consisted of several object-based features computed from spectral bands and including mean values, spectral indices and textural features. S2 and L8 data comparisons were also extended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery to test differences only due to their specific spectral contribution. The best band combinations to perform segmentation were found through a modified version of the Euclidian Distance 2 index. Four different RF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overall accuracies respectively, evaluated over the whole study area.

Automated detection of cerebral microbleeds in patients with Traumatic Brain Injury.

In this paper a Computer Aided Detection (CAD) system is presented to automatically detect Cerebral Microbleeds (CMBs) in patients with Traumatic Brain Injury (TBI). It is believed that the presence of CMBs has clinical prognostic value in TBI patients. To study the contribution of CMBs in patient outcome, accurate detection of CMBs is required. Manual detection of CMBs in TBI patients is a time consuming task that is prone to errors, because CMBs are easily overlooked and are difficult to distinguish from blood vessels.This study included 33 TBI patients. Because of the laborious nature of manually annotating CMBs, only one trained expert manually annotated the CMBs in all 33 patients. A subset of ten TBI patients was annotated by six experts. Our CAD system makes use of both Susceptibility Weighted Imaging (SWI) and T1 weighted magnetic resonance images to detect CMBs. After pre-processing these images, a two-step approach was used for automated detection of CMBs. In the first step, each voxel was characterized by twelve features based on the dark and spherical nature of CMBs and a random forest classifier was used to identify CMB candidate locations. In the second step, segmentations were made from each identified candidate location. Subsequently an object-based classifier was used to remove false positive detections of the voxel classifier, by considering seven object-based features that discriminate between spherical objects (CMBs) and elongated objects (blood vessels). A guided user interface was designed for fast evaluation of the CAD system result. During this process, an expert checked each CMB detected by the CAD system.A Fleiss' kappa value of only 0.24 showed that the inter-observer variability for the TBI patients in this study was very large. An expert using the guided user interface reached an average sensitivity of 93%, which was significantly higher (p = 0.03) than the average sensitivity of 77% (sd 12.4%) that the six experts manually detected. Furthermore, with the use of this CAD system the reading time was substantially reduced from one hour to 13 minutes per patient, because the CAD system only detects on average 25.9 false positives per TBI patient, resulting in 0.29 false positives per definite CMB finding.

An Automatic Cognitive Graph-Based Segmentation for Detection of Blood Vessels in Retinal Images

This paper presents a hierarchical graph-based segmentation for blood vessel detection in digital retinal images. This segmentation employs some of perceptual Gestalt principles: similarity, closure, continuity, and proximity to merge segments into coherent connected vessel-like patterns. The integration of Gestalt principles is based on object-based features (e.g., color and black top-hat (BTH) morphology and context) and graph-analysis algorithms (e.g., Dijkstra path). The segmentation framework consists of two main steps: preprocessing and multiscale graph-based segmentation. Preprocessing is to enhance lighting condition, due to low illumination contrast, and to construct necessary features to enhance vessel structure due to sensitivity of vessel patterns to multiscale/multiorientation structure. Graph-based segmentation is to decrease computational processing required for region of interest into most semantic objects. The segmentation was evaluated on three publicly available datasets. Experimental results show that preprocessing stage achieves better results compared to state-of-the-art enhancement methods. The performance of the proposed graph-based segmentation is found to be consistent and comparable to other existing methods, with improved capability of detecting small/thin vessels.

High-resolution Land Cover and Impervious Surface Classifications in the Twin Cities Metropolitan Area with NAIP Imagery

High-resolution land-use and land-cover (lulc) and impervious surface maps were traditionally created by time-consuming field surveys or aerial photo interpretation and digitizing methods. In the United States, the recent availability of 1 m digital color orthoimagery through the National Agriculture Imagery Program (naip) offers an opportunity to update land cover maps for areas of interest. In this study, an object-based feature extraction and a regression tree technique were applied to the NAIP imagery for the Twin Cities Metropolitan Areas (tcma) of Minnesota. An overall accuracy of 74 percent and 95 percent was achieved for the general LULC classification and the impervious surface map, respectively. It was found that both techniques could be used to extract impervious surfaces from the NAIP imagery with relatively high accuracy. The LULC classification results were compared to the 2006 National Land Cover Database (nlcd) and another Landsat-based 2006 classification. Implications for future classification studies were also discussed.

A multi-sensor approach for detecting the different land covers of tidal flats in the German Wadden Sea — A case study at Norderney

The tidal flats of the Wadden Sea are a highly dynamic and largely natural ecosystem with high economic and ecological value but which are also at risk due to climate change, rising sea levels, algae blooms, invasive species and marine pollution. There is a need for the detection of emerging changes and the potential loss of the natural or semi-natural ecosystems accompanied by a decrease in water quality. Accessibility both from sea and land is very poor, which makes the monitoring and mapping of tidal flat environments from in situ measurements very difficult. In this study, a multi-sensor concept for the classification of intertidal areas in the Wadden Sea is developed. The basis for this method is a combined analysis involving RapidEye (RE) and TerraSAR-X (TSX) satellite data combined with ancillary vector data about the distribution of vegetation, shellfish beds and sediments for the accuracy assessment. The overall methodology is based on a hierarchical decision tree. First, the water coverage is separated from the tidal flats by using the normalized difference water index (NDWI). Second, the shellfish beds are estimated with the textural features of the TSX data and morphologic filters (MFs). Third, the classification of vegetation (salt marsh, sea grass/algae) is based on the modified soil-adjusted vegetation index (MSAVI), object-based features and exclusionary criteria. The remaining area is then separated into different sediment types with an algorithm that uses a thresholding technique applied to radiometric values, the MSAVI and a majority filter. The results show that we are able to identify the location and shape of salt marsh and shellfish beds (a true positive rate of 0.63 and a precision of 0.55) by using multi-sensor remote sensing data. A detailed shellfish bed classification can only be done with radar sensors, like TSX. The extraction of the sea grass areas from the multi-sensor approach is difficult. Sea grass often grows very sparsely in the study area which, with respect to the spatial resolution of RE, leads to a mixture of the spectral signatures of sediment and sea grass. The classification of the sediment types in tidal flats is a challenge compared to vegetation and shellfish beds. An overall accuracy of 64.53%, 66.85%, 68.22% and 68.2% was achieved. The results emphasize that a sediment-type classification cannot be achieved with high accuracy using spectral properties alone due to their similarity, which is predominately caused by their water content.

Measuring the Effectiveness of Various Features for Thematic Information Extraction From Very High Resolution Remote Sensing Imagery

Generally, some object-based features are more relevant to a thematic class than other features. These strongly relevant features, termed as class-specific features, would significantly contribute to thematic information extraction for very high resolution (VHR) images. However, many existing feature selection methods have been designed to select a good feature subset for all classes, rather than an independent feature subset for the thematic class. The latter might better meet the requirement of thematic information extraction than the former. In addition, the lack of quantitative evaluation of the contribution of the selected features to thematic classes also weakens our understandability of these features. To address the problems, class-specific feature selection methods are developed to measure the effectiveness of features for extracting thematic information from VHR images. First, the one-versus-all scheme is combined with traditional feature selection methods, such as ReliefF and LeastC. Also, one-versus-one scheme is utilized for alleviating the negative impact of a class imbalance problem arising from the one-versus-all scheme. Then, the relative contributions of features to thematic classes are obtained by the class-specific feature selection methods to describe the effectiveness of features for thematic information extraction. Finally, the class-specific feature selection methods are compared with the original methods on three different VHR image data sets by the nearest neighbor and support vector machine. Experimental results show that the class-specific feature selection methods outperform the corresponding conventional methods, and the one-versus-one scheme surpasses one-versus-all scheme. Additionally, many features are evaluated by the class-specific feature selection methods, to provide end users advice on effectiveness of the features.

Rule Set Transferability for Object-Based Feature Extraction: An Example for Cirque mapping

AbstractCirques are complex landforms resulting from glacial erosion and can be used to estimate Equilibrium Line Altitudes and infer climate history. Automated extraction of cirquesmay help research on glacial geomorphology and climate change. Our objective was to test the transferability of an object-based rule set for the extraction of glacial cirques, using lidar data and color-infrared orthophotos. In Vorarlberg (W-Austria), we selected one training area with well-developed cirque components to parameterize segmentation and classification criteria. The rule set was applied to three test areas that are positioned in three altitudinal zones. Results indicate that the rule set was successful (81 percent) in the training area and a higher situated area (71 percent). Accuracy decreased in the two lower situated test areas (66 percent and 51 percent). We conclude that rule sets are transferable to areas with a comparable geomorphological history. Yet, significant deviation from the training area requires a different extraction strategy.

Object-based attention underlies the rehearsal of feature binding in visual working memory.

Feature binding is a core concept in many research fields, including the study of working memory (WM). Over the past decade, it has been debated whether keeping the feature binding in visual WM consumes more visual attention than the constituent single features. Previous studies have only explored the contribution of domain-general attention or space-based attention in the binding process; no study so far has explored the role of object-based attention in retaining binding in visual WM. We hypothesized that object-based attention underlay the mechanism of rehearsing feature binding in visual WM. Therefore, during the maintenance phase of a visual WM task, we inserted a secondary mental rotation (Experiments 1-3), transparent motion (Experiment 4), or an object-based feature report task (Experiment 5) to consume the object-based attention available for binding. In line with the prediction of the object-based attention hypothesis, Experiments 1-5 revealed a more significant impairment for binding than for constituent single features. However, this selective binding impairment was not observed when inserting a space-based visual search task (Experiment 6). We conclude that object-based attention underlies the rehearsal of binding representation in visual WM.

Sea Oil Spill Detection Method Using SAR Imagery Combined with Object-Based Image Analysis and Fuzzy Logic

Synthetic aperture radar (SAR), a sensor with all weather and day and night working capacity, has been considered one of the most powerful tools for sea surface oil spill detection. However, lookalikes frequently appear in SAR images, limiting the operational use of SAR to detect oil spilled at sea. 20 scenes of Envisat ASAR images, which were acquired during the oil spill accident in the Gulf of Mexico in 2010, are utilized, with the objective to study how to better differentiate oil spills from lookalikes. 145 and 134 samples for oil spill and lookalike, respectively, are extracted, and their object-based geometric, physical and textural features are analyzed, in order to find the most effective features for oil spill classification. Based on the results of feature analysis, fuzzy logic (FL) is employed to construct a classifier for oil spill detection. One advantage of the proposed method is that it can produce the crisp probability of a dark segment being oil spill. The experiment shows that our method can derive promising result.

Woody vegetation and land cover changes in the Sahel of Mali (1967–2011)

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change.

Estimating potential outdoor water consumption in private urban landscapes by coupling high-resolution image analysis, irrigation water needs and evaporation estimation in Spain

In the past two decades, the Spanish Mediterranean coast has experienced urban and demographic growth through the expansion of low-density residential areas characterized by ornamental gardens, swimming pools, and lawns. To determine the potential outdoor water demand, land cover data were mapped from high-resolution WorldView-2 imagery along a climatic gradient from Mallorca, in the Balearic Islands, to Andalusia in Spain. WorldView-2 imagery was analyzed by adopting object-based feature extraction to generate spatial information layers as additional input features (predictor variables) for supervised per-pixel classification. Swimming pools and gardens planted with turf grass, trees and shrubs were mapped automatically with high levels of overall accuracy, ranging from 88.98% to 96.13%. The resulting GIS (geographic information systems)-ready vector layer was used to calculate net irrigation requirements with the landscape coefficient method and to assess evaporative loss from swimming pools. The method and results can be used by urban planners, water managers, property owners and others to assess the implications of landscaping styles for water demand. Net irrigation requirements (April–September) range from 2335m3/ha to 6330m3/ha compared to 5400m3/ha per year in irrigated agriculture in Spain. Per unit area, the same outdoor landscaping produces a 45.5% higher net irrigation requirement and 21.6% more evaporation from swimming pools along the climatic gradient. The combination of high-resolution image analysis and the landscape coefficient method captures the relevant information for the identification and quantification of outdoor water consumption in private urban landscapes.

High Spatial-Resolution Land Cover Classification and Wetland Mapping over Large Areas Using Integrated Geospatial Technologies

Land Use and Land Cover (LULC) and wetland classification maps are an important prerequisite for many environmental studies. In order to produce accurate LULC and wetland maps at high spatialresolution, a new approach was developed to integrate image classifications, spatial data layers, and analysis methods using Python scripting. Both Maximum Likelihood and Object-based Feature Extraction were adopted into the LULC classification. A spatial analysis approach was applied to wetland mapping based on available wetland inventories and soil data. Python scripts were created and used to automate these processes for each of the 30 reference sites across Minnesota and Wisconsin of the United States, which encompassed the entire study site. Results demonstrated that the proposed method allowed for the integration of geospatial data of varying sources and qualities to produce accurate LULC and wetland maps effectively. The results of accuracy assessment indicated that the classification maps for Minnesota and Wisconsin were of comparable quality. The objectbased classifier extracted LULC effectively from the Wisconsin imagery with acceptable accuracy despite lacking of the NIR spectral band. These maps were used as inputs to create a hydro geomorphicap-proach (HGM) guidebook (Hauer and Smith 1998) for both states (Cook et al. unpublished). The Python-based technique was found to be especially beneficial when dealing with big datasets over large study areas, as it allowed batch processing.

Cross-cultural differences in memory specificity

Attention and memory have been shown to differ across cultures, with independent Western cultures preferring an object-based feature analysis and interdependent Eastern cultures preferring a context-based holistic analysis. In two experiments, we assessed whether these cultural differences not only affect how much information is remembered, but also the specificity of memory such that the feature analysis preference of Americans should lead to greater memory for visual detail. Americans and East Asians incidentally encoded pictures of single items (Exp 1) and pictures of focal items presented against a meaningful background (Exp 2). On a recognition test, participants made same, similar, or new decisions about items (Exp 1 & 2) and backgrounds (Exp 2) that were the same as or similar to the encoded stimuli, as well as novel lures. As predicted, Americans exhibited greater accuracy than East Asians in specific memory for objects presented alone and this trend continued across both objects and backgrounds when objects were depicted in context. The two cultural groups did not differ in general (item-level) memory. These results support the idea that the feature analysis biases of Westerners may increase the specificity of visual information contained in memory, despite equivalent item-level memory.