Assessing image segmentation quality – concepts, methods and application

Neubert, Marco; Herold, Hendrik; Meinel, Gotthard

doi:10.1007/978-3-540-77058-9_42

Cited by 91 publications

(56 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first GEOSS key principle is quantitative (unequivocal), has nothing to do with meaning and is related to the Shannon concept of 'information-as-thing' [3]. Therefore, it is easier to deal with than the second GEOSS principle, which is qualitative (equivocal), has to deal with the meaning (interpretation, understanding) of (quantitative) data and is related to the concept of 'information-as-(an interpretation)process' [11,12]. In the words of philosophical hermeneutics, "there is no knowledge without both an object of knowledge and a knowing subject.…”

Section: Problem Recognition and Opportunity Identificationmentioning

confidence: 99%

“…The present first part identifies possible causes of the lack of productivity affecting existing academic and commercial RS image understanding systems (RS-IUSs) outpaced by the ever-increasing rate of collection of spaceborne and airborne sensory data. To reach its objective, this contribution adopts a holistic convergence-of-evidence approach to provide an inter-disciplinary analysis of biological vision, computer vision (CV), artificial intelligence (AI), machine learning (MAL) and RS-IUS design and implementation, with special emphasis on state-of-the-art two-stage non-iterative geographic (2-D) object-based image analysis (GEOBIA) systems [6][7][8][9][10][11] and three-stage iterative geographic (2-D) object-oriented image analysis (GEOOIA) systems [6], where GEOBIA is a special case of GEOOIA, i.e., GEOOIA ⊃ GEOBIA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

2012

View full text Add to dashboard Cite

According to existing literature and despite their commercial success, state-of-the-art two-stage non-iterative geographic object-based image analysis (GEOBIA) systems and three-stage iterative geographic object-oriented image analysis (GEOOIA) systems, where GEOOIA ⊃ GEOBIA, remain affected by a lack of productivity, general consensus and research. To outperform the degree of automation, accuracy, efficiency, robustness, scalability and timeliness of existing GEOBIA/GEOOIA systems in compliance with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines, this methodological work is split into two parts. The first part of this work provides a multi-disciplinary Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of the GEOBIA/GEOOIA approaches that augments similar analyses proposed in recent years. In line with constraints stemming from human vision, this SWOT analysis promotes a shift of learning paradigm in the pre-attentive vision first stage of a remote sensing (RS) image understanding system (RS-IUS), from sub-symbolic statistical model-based (inductive) image segmentation to symbolic physical model-based (deductive) image preliminary classification. Hence, a symbolic deductive pre-attentive vision first stage accomplishes image sub-symbolic segmentation and image symbolic pre-classification simultaneously. In the second part of this work a novel hybrid (combined deductive and inductive) RS-IUS architecture featuring a symbolic deductive pre-attentive vision first stage is proposed and discussed in terms of: (a) computational theory (system design); (b) information/knowledge representation; (c) algorithm design; and OPEN ACCESSRemote Sens. 2012, 4 2695 (d) implementation. As proof-of-concept of symbolic physical model-based pre-attentive vision first stage, the spectral knowledge-based, operational, near real-time Satellite Image Automatic Mapper™ (SIAM™) is selected from existing literature. To the best of these authors' knowledge, this is the first time a symbolic syntactic inference system, like SIAM™, is made available to the RS community for operational use in a RS-IUS pre-attentive vision first stage, to accomplish multi-scale image segmentation and multi-granularity image pre-classification simultaneously, automatically and in near real-time.

show abstract

Section: Problem Recognition and Opportunity Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

2012

View full text Add to dashboard Cite

show abstract

“…Different algorithms have been developed [42], first and foremost the multi-resolution image segmentation implemented in the software eCognition [43]. This bottom-up, region-growing approach aggregates surrounding pixels according to pre-defined criteria of homogeneity through a combination of different parameters, i.e., scale, color and shape.…”

Section: Geographic Object-based Image Analysismentioning

confidence: 99%

Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo

2014

View full text Add to dashboard Cite

Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of the Congo (DRC) to identify hot spots of mining activities and provide reliable information about the situation in and around two selected mining sites, Mumba-Bibatama and Bisie. The first is the test case for the approach and the detection of unknown mining sites, whereas the second acts as reference case since it is the largest and most well-known location for cassiterite extraction in eastern Congo. Thus it plays a key-role within the context of the conflicts in this region. Detailed multi-temporal analyses of very high-resolution (VHR) satellite data demonstrates the capabilities of Geographic Object-Based Image Analysis (GEOBIA) techniques for providing information about the situation during a mining ban announced by the Congolese President between September 2010 and March 2011. Although the opening of new surface patches can serve as an indication for activities in the area, the pure change between the two satellite images does not in itself produce confirming evidence. However, in combination with observations on the ground, it becomes evident that mining activities continued in Bisie during the ban, even though the production volume went down considerably.

show abstract

“…Except for ENVI, IDL and eCognition all other software used is free and open source [24]. Though we note that GDL [25], a GNU version of IDL is freely available, as are numerous segmentation software, both online [26], and as described by Neubert et al [27].…”

Section: Platform Designmentioning

confidence: 99%

Geospatial Technologies to Improve Urban Energy Efficiency

et al. 2011

View full text Add to dashboard Cite

Abstract:The HEAT (Home Energy Assessment Technologies) pilot project is a FREE Geoweb mapping service, designed to empower the urban energy efficiency movement by allowing residents to visualize the amount and location of waste heat leaving their homes and communities as easily as clicking on their house in Google Maps. HEAT incorporates Geospatial solutions for residential waste heat monitoring using Geographic Object-Based Image Analysis (GEOBIA) and Canadian built Thermal Airborne Broadband Imager technology (TABI-320) to provide users with timely, in-depth, easy to use, location-specific waste-heat information; as well as opportunities to save their money and reduce their green-house-gas emissions. We first report on the HEAT Phase I pilot project which evaluates 368 residences in the Brentwood community of Calgary, Alberta, Canada, and describe the development and implementation of interactive waste heat maps, energy use models, a Hot Spot tool able to view the 6+ hottest locations on each home and a new HEAT Score for inter-city waste heat comparisons. We then describe current challenges, lessons learned and new solutions as we begin Phase II and scale from 368 to 300,000+ OPEN ACCESSRemote Sens. 2011, 3 1381 homes with the newly developed TABI-1800. Specifically, we introduce a new object-based mosaicing strategy, an adaptation of Emissivity Modulation to correct for emissivity differences, a new Thermal Urban Road Normalization (TURN) technique to correct for scene-wide microclimatic variation. We also describe a new Carbon Score and opportunities to update city cadastral errors with automatically defined thermal house objects.

show abstract

Assessing image segmentation quality – concepts, methods and application

Cited by 91 publications

References 13 publications

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

Operational Automatic Remote Sensing Image Understanding Systems: Beyond Geographic Object-Based and Object-Oriented Image Analysis (GEOBIA/GEOOIA). Part 1: Introduction

Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo

Geospatial Technologies to Improve Urban Energy Efficiency

Contact Info

Product

Resources

About