An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

Witharana, Chandi; Lynch, Heather J.

doi:10.3390/rs8050375

Cited by 38 publications

(40 citation statements)

References 73 publications

(72 reference statements)

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“…Chabot and Francis () reported that automated counts of waterbirds were within 3%–5% of human counts across 16 applications. Recent improvements of UAV‐based counting include utilizing hyperspectral data (Beijboom et al., ; Witharana & Lynch, ), pixel‐shape modelling (Liu et al., ) and combining background subtraction with machine learning (Torney et al., ) (Figure b). Recent efforts to count animals use deep learning neural networks are promising, but require tens of thousands of training images gathered by human annotation (Bowley et al., ).…”

Section: Countingmentioning

confidence: 99%

A computer vision for animal ecology

Weinstein

2017

Journal of Animal Ecology

316

287

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A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.

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Section: Countingmentioning

confidence: 99%

A computer vision for animal ecology

Weinstein

2017

Journal of Animal Ecology

316

287

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“…These oversegmented results were then fed into the chessboard segmentation algorithm to be fine-tuned and merged, resulting in more appropriate final segmentation results without directly optimizing the SP. In a similar study, Witharana and Lynch [31] combined the segmentation derived from the multi-threshold segmentation (MTS) algorithm with that derived from the MRS to improve segmentation results without applying an intensive trial-and-error process. In their method, MTS is first applied to the image to generate undersegmented, simplified image objects.…”

Section: Related Workmentioning

confidence: 99%

“…Studies on parametrizing SP can be categorized into two general groups: supervised approaches [21,30,31,40], and unsupervised approaches [17,20,22,29]. With supervised methods, the goal is to identify an optimal segmentation (or set of segmentations) by evaluating the overlap of ground-truth reference polygons and computer-generated image segments.…”

Section: Related Workmentioning

confidence: 99%

“…In practice, multiple segmentation parameters are usually tested and compared, and "suitable" segmentation parameters (i.e., those that do not cause excessive over-or undersegmentation) are determined either manually (by trial and error [25][26][27]) or automatically (e.g., using supervised or unsupervised parameter selection algorithms [17,20,22,[28][29][30][31]). Although manual parameter selection through visual assessment could lead to the most optimal segmentation results if done by a skilled image analyst, the process is typically time consuming and/or computationally intensive.…”

Section: Segmentation Parameter Selectionmentioning

confidence: 99%

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Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

Johnson

Jozdani

2018

Remote Sensing

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Abstract:The advent of very high resolution (VHR) satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA) have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS) algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters). The scale parameter (SP) is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific) could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water) through meta-analysis and nonlinear regression tree (RT) modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions), and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our approach over existing SP selection approaches is that our RT model results are not scene-specific, so they can be used to quickly identify suitable SPs in other VHR images.

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“…In order to compare the accuracies of the two classification results, three commonly used evaluation metrics were adopted: accuracy, precision, and recall [27,54,55] …”

Section: Accuracy Assessment and Comparisonmentioning

confidence: 99%

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

Zheng

Weston

et al. 2017

Remote Sensing

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Multiple policy projects have changed land use and land cover (LULC) in China's rural regions over the past years, resulting in two types of rural settlements: new-fashioned and old-fashioned. Precise extraction of and discrimination between these two settlement types are vital for sustainable land use development. It is difficult to identify these two types via remote sensing images due to their similarities in spectrum, texture, and geometry. This study attempts to discriminate different types of rural settlements by using a spatial contextual information extraction method based on Gaofen 2 (GF-2) images, which integrate hierarchical multi-scale segmentation and landscape analysis. A preliminary LULC map was derived by using only traditional spectral and geometrical features from a finer scale. Subsequently, a vertical connection was built between superobjects and subobjects, and landscape metrics were computed. The vertical connection was used for assigning landscape contextual information to subobjects. Finally, a classification phase was conducted, in which only multi-scale contextual information was adopted, to discriminate between new-fashioned and old-fashioned rural settlements. Compared with previous studies on multi-scale contextual information, this paper employs landscape metrics to quantify contextual characteristics, rather than traditional spectral, textural, and topological relationship information, from superobjects. Our findings indicate that this approach effectively identified and discriminated two types of rural settlements, with accuracies over 80% for both producers and users. A comparison with a conventional top-down hierarchical classification scheme showed that this novel approach improved accuracy, precision, and recall. Our results confirm that multi-scale contextual information with landscape metrics provides valuable spatial information for classification, and indicates the practicability, applicability, and effectiveness of this synthesized approach in distinguishing different types of rural settlements.

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An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

Cited by 38 publications

References 73 publications

A computer vision for animal ecology

A computer vision for animal ecology

Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

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