Robust Extraction of Urban Land Cover Information From HSR Multi-Spectral and LiDAR Data

Berger, Christian; Voltersen, M.; Hese, S.; Walde, I.; Schmullius, C.

doi:10.1109/jstars.2013.2252329

Cited by 27 publications

(9 citation statements)

References 114 publications

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“…R EMOTE SENSING images (RSIs) are often used as data source for land cover studies in many applications (e.g., agriculture [1] and urban planning [2]). A common challenge in these applications relies on the definition of the representation scale 1 of the data (size of the segmented regions or block of pixels) [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient and Effective Hierarchical Feature Propagation

Santos

Penatti

Gosselin

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Many methods have been recently proposed to deal with the large amount of data provided by the new remote sensing technologies. Several of those methods rely on the use of segmented regions. However, a common issue in region-based applications is the definition of the appropriate representation scale of the data, a problem usually addressed by exploiting multiple scales of segmentation. The use of multiple scales, however, raises new challenges related to the definition of effective and efficient mechanisms for extracting features. In this paper, we address the problem of extracting features from a hierarchy by proposing two approaches that exploit the existing relationships among regions at different scales. The H-Propagation propagates any histogram-based low-level descriptors. The bag-of-visualword (BoW)-Propagation approach uses the BoWs model to propagate features along multiple scales. The proposed methods are very efficient, as features need to be extracted only at the base of the hierarchy and yield comparable results to low-level extraction approaches.

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Section: Introductionmentioning

confidence: 99%

Efficient and Effective Hierarchical Feature Propagation

Santos

Penatti

Gosselin

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Specifically, we aimed to Remote Sens. 2018, 10, 73 5 of 29 reduce the time/effort needed for setting appropriate MRS parameters for different urban land cover types, with a focus on VHR (<5 m) imagery, which was previously shown to be effective for urban mapping [37,38]. As reported by Cowen et al [39], to recognize an urban object in a given remotely sensed image, the spatial resolution should be at least one-half of the smallest object to be extracted.…”

Section: Objective Of This Studymentioning

confidence: 99%

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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“…Land cover information about the earth's surface is critical in most earth and environmental engineering applications (Berger et al, 2013). To name a few examples, one can consider different studies on urban structure (Voltersen et al, 2014), detection of urban objects e.g.…”

Section: Introductionmentioning

confidence: 99%

A New Framework for Object-Based Image Analysis Based on Segmentation Scale Space and Random Forest Classifier

Hadavand

Saadatseresht

Homayouni

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4 KEY WORDS: Segmentation, Scale parameter, Object-based Image Analysis, Land Cover Classification. ABSTRACT:In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

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Robust Extraction of Urban Land Cover Information From HSR Multi-Spectral and LiDAR Data

Cited by 27 publications

References 114 publications

Efficient and Effective Hierarchical Feature Propagation

Efficient and Effective Hierarchical Feature Propagation

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

A New Framework for Object-Based Image Analysis Based on Segmentation Scale Space and Random Forest Classifier

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