Unsupervised evaluation-based region merging for high resolution remote sensing image segmentation
TL;DR: A new segmentation technique by fusing a region merging method with an unsupervised segmentation evaluation technique called under- and over-segmentation aware (UOA), which is improved by using edge information is presented.
Abstract: Image segmentation has a remarkable influence on the classification accuracy of object-based image analysis. Accordingly, how to raise the performance of remote sensing image segmentation is a key ...
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TL;DR: A new region merging method by using a random forest (RF) classifier, which relies on a trained RF to decide the result of a merging test, and a sample collection strategy based on a set of three-scale segmentation results is devised.
Abstract: With the increasing popularity of OBIA, many scholars advocate that image segmentation plays a significant role in remote sensing image processing Numerous segmentation algorithms for remote sensing images are based on region merging Although good improvement is achieved, their accuracy is still dependent on parameter settings, leading to a low level of automation To overcome this issue, this work proposes a new region merging method by using a random forest (RF) classifier Unlike the traditional region merging methods that all adopt a scale threshold to determine whether a merging can be conducted, the new algorithm relies on a trained RF to decide the result of a merging test Various merging criteria are simultaneously employed as feature variables of the RF model, enhancing the quality of the proposed scheme The major problem in this work is how to train the RF classifier since the merging test samples need to be obtained in the iterative steps of a region merging process, which involves a huge number of human–computer interactions even for a small image To simplify it, a sample collection strategy based on a set of three-scale segmentation results is devised Representative merging test samples can be obtained by using this method To validate the proposed technique, four Gaofen-2 images are used for training sample collection, and the most interesting result is that the samples extracted from one image can apply to others Some images captured by Orbview-3, GeoEye-1, and Worldview-2 further indicate the robust performance of the new algorithm and the samples acquired in this work
17 citations
TL;DR: In this paper, double-variance (DV) measures were proposed for recognizing more suitable SPs and two combination strategies, F-measure and local peak (LP), were applied to test the potential of using DV measures to determine a single SP and multiple SPs, respectively.
Abstract: The unsupervised segmentation evaluation (USE) method has been commonly used for remote sensing segmentation parameter (SP) determinations to produce good segmentation results, due to its objectiveness and high efficiency. Existing studies have used different criteria to measure homogeneity and heterogeneity and have used certain combination strategies to form overall evaluations. However, different criteria have unique statistical characteristics. The differentiated statistical characteristics maintained in homogeneity and heterogeneity calculations may result in inherent instability in the USE results, leading to unsuitable SP selections. Moreover, few studies have focused on the simultaneous determination of a single optimal SP and multiple optimal SPs. In this article, double-variance (DV) measures were proposed for recognizing more suitable SPs. Then, two combination strategies, F-measure and local peak (LP), were applied to test the potential of using DV measures to determine a single SP and multiple SPs, respectively. The multiresolution segmentation algorithm and Gaofen-1 data were used to test the proposed method. The comparative results indicated that the DV is a more promising internal homogeneity and external heterogeneity metric for segmentation evaluation and optimal SP determination compared to conventional methods. The F-measure-based DV method could produce better overall goodness of segmentation for differently sized natural geo-objects, compared with the competing methods. The LP-based DV method could obtain multiple optimal scales that produced better segments for the identification of small, natural geo-objects to large, semantic geo-objects, compared to the competitive methods.
4 citations
TL;DR: Li et al. as mentioned in this paper developed a hybrid image segmentation method with local scale-guided hierarchical region merging and further over- and under-segmentation processing, where the primitive segmentation was produced and then stratified into layers with different land covers.
Abstract: With the development of medium- and high-resolution satellites, successfully segmenting differently sized geo-objects remains a challenging issue for geographic object-based image analysis (GEOBIA). The hybrid image segmentation method is a good alternative to produce good segmentation that best matched the different sizes of geo-objects. However, the existing methods almost use segmentation parameters (SPs), such as scale, to control the sizes and shapes of segments. This will lead to two issues: (1) one single scale is impossible to segment every geo-object well due to the land cover complexity within remote-sensing imageries; (2) over- and under-segmented regions still occur in the segmentation results, whatever using any advanced segmentation methods. To solve the above problems, this paper developed a hybrid image segmentation method with local scale-guided hierarchical region merging and further over- and under-segmentation processing. First, the primitive segmentation was produced and then stratified into layers with different land covers. Then, the local scale was calculated for a more objective merging process in the separating layers. Third, the over- and under-segmentation at separating layers was recognized and re-processed for achieving a fine segmentation. To validate the proposed method, it was applied to three test images of gaofen-1 satellite with different land cover types, and ten competing methods were compared. The visual and quantitative results indicated the advantage of our method in segmenting out different sizes of geo-objects, which can effectively reduce the over- and under-segmentation error.
1 citations
TL;DR: In this article , an object-based multiscale segmentation (MSS) algorithm combining spectral, shape, texture, and edge features is proposed for remote sensing image texture feature description based on time-frequency analysis.
Abstract: Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction.
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TL;DR: This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way.
Abstract: Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.
3,809 citations
"Unsupervised evaluation-based regio..." refers background in this paper
...Object-based image analysis (OBIA), which has received considerable attention in remote sensing community (Blaschke 2010; Blaschke et al. 2014; Chen and Weng 2018), provides a promising solution (Chen et al. 2018)....
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...Object-based image analysis (OBIA), which has received considerable attention in remote sensing community (Blaschke 2010; Blaschke et al. 2014; Chen and Weng 2018), provides a promising solution (Chen et al....
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TL;DR: This correspondence presents a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters, and suggests two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.
Abstract: We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures. >
3,331 citations
"Unsupervised evaluation-based regio..." refers methods in this paper
...Many methods, such as hierarchical stepwise optimization (Beaulieu and Goldberg 1989), seeded region growing (Adams and Bischof 1994), segmentation engine (Gofman 2006), size-constrained region merging (Castilla, Hay, and Ruiz 2008), iterative region growing using semantics (Yu and Clausi 2008; Qin…...
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TL;DR: An optimized tree is defined and an algorithm to accomplish optimization in n log n time is presented, guaranteeing that Searching is guaranteed to be fast in optimized trees.
Abstract: The quad tree is a data structure appropriate for storing information to be retrieved on composite keys. We discuss the specific case of two-dimensional retrieval, although the structure is easily generalised to arbitrary dimensions. Algorithms are given both for staightforward insertion and for a type of balanced insertion into quad trees. Empirical analyses show that the average time for insertion is logarithmic with the tree size. An algorithm for retrieval within regions is presented along with data from empirical studies which imply that searching is reasonably efficient. We define an optimized tree and present an algorithm to accomplish optimization in n log n time. Searching is guaranteed to be fast in optimized trees. Remaining problems include those of deletion from quad trees and merging of quad trees, which seem to be inherently difficult operations.
2,048 citations
"Unsupervised evaluation-based regio..." refers methods in this paper
...Quad-treebased technique (Finkel and Bentley 1974; Wu, Hong, and Rosenfeld 1982) is an example....
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TL;DR: In this paper, the authors discuss the limitations of prevailing per-pixel methods when applied to high-resolution images and explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition.
Abstract: The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.
1,231 citations
"Unsupervised evaluation-based regio..." refers background in this paper
...Object-based image analysis (OBIA), which has received considerable attention in remote sensing community (Blaschke 2010; Blaschke et al. 2014; Chen and Weng 2018), provides a promising solution (Chen et al. 2018)....
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...Object-based image analysis (OBIA), which has received considerable attention in remote sensing community (Blaschke 2010; Blaschke et al. 2014; Chen and Weng 2018), provides a promising solution (Chen et al....
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TL;DR: The results based on a QuickBird satellite image indicate that segmentation accuracies decrease with increasing segmentation scales and the negative impacts of under-segmentation errors become significantly large at large scales.
Abstract: The advantages of object-based classification over the traditional pixel-based approach are well documented. However, the potential limitations of object-based classification remain less explored. In this letter, we assess the advantages and limitations of an object-based approach to remote sensing image classification relative to a pixel-based approach. We first quantified the negative impacts of under-segmentation errors on the potential accuracy of object-based classification by developing a new segmentation accuracy measure. Then we evaluated the advantages and limitations of object-based classification by quantifying their overall effects relative to pixel-based classification, with respect to their classification units and features at multiple segmentation scales. The results based on a QuickBird satellite image indicate that (1) segmentation accuracies decrease with increasing segmentation scales and the negative impacts of under-segmentation errors become significantly large at large scales and (2...
350 citations
"Unsupervised evaluation-based regio..." refers result in this paper
...Such results are quite consistent with previous studies (Grybas, Melendy, and Congalton 2017; Liu, Du, and Mao 2017; Liu and Xia 2010)....
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