Human-centered concepts for exploration and understanding of Earth observation images

Datcu, Mihai; Seidel, K.

doi:10.1109/tgrs.2005.843253

Cited by 66 publications

(36 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the widespread availability of meter-resolution remote sensing images, it is necessary to develop a method to explore and understand the contents of large and highly complex images [35,36]. In the last decade, considerable research has been devoted to introducing human knowledge into the interpretation of remote sensing data [35][36][37].…”

Section: Ontological Semantic Analysismentioning

confidence: 99%

“…In the last decade, considerable research has been devoted to introducing human knowledge into the interpretation of remote sensing data [35][36][37]. The knowledge representation system is promising to organize multiple information of remote sensing image [37] and import some high-level semantics into the process of interpretation.…”

Section: Ontological Semantic Analysismentioning

confidence: 99%

See 1 more Smart Citation

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

Gui

Hui

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Accurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations.

show abstract

Section: Ontological Semantic Analysismentioning

confidence: 99%

Section: Ontological Semantic Analysismentioning

confidence: 99%

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

Gui

Hui

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…We also create an accumulator image A the same size as C; A is initialized to 0. The stack filters pseudo-code [2].…”

Section: Change Detectionmentioning

confidence: 99%

“…For example, utilization of clustering algorithms is to discover different classes of land cover in a geospatial image. Tyagi et al [2] proposed a context-sensitive clustering approach using graph-cut initialization and an expectation maximization (EM) algorithm for classifying pixels from a multispectral (MS) Landsat-5 image into different classes of land cover, while Maulik and Saha [3] proposed the use of modified differential-evolution-based fuzzy clustering. Yang et al used the fuzzy statistics similarity as a metric in an affinity propagation clustering algorithm to extract land cover information from Landsat-7, Quick bird, and MODIS data sets [4].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Satellite Imagery Changes Detection using Agglomerative Fuzzy K-Means Clustering Algorithm

Pandimuthu¹,

Kuppusamy²

2012

IJCA

View full text Add to dashboard Cite

The high-resolution commercial satellite imagery (HRCSI) has increased significantly over the last 5 years for a wide variety of applications. This has increase in volume, frequency of acquisition, and spatial resolution of HRCSI. In particular, satellite images contain land cover types; large areas (e.g., building, bridge and roads) occupy relatively small regions. The change detection and exploitation of change between multi temporal highresolution satellite and air bone images. Overlapping multi temporal images are first organized in to 256m x 256m tiles in a global grid reference system. The tiles are initially ranged by these changes scores for retrieval, review, and exploitation in web based applications. Automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper we present an agglomerative fuzzy K-Means clustering algorithm in change detection. The algorithm can produce more consistent clustering result from different sets of initial clusters centres, the algorithm determine the number of clusters in the data sets, which is a well -known problem in K-means clustering.

show abstract

“…For instance, the German Aerospace Center (DLR) expects to launch along year 2014 a hyperspectral satellite mission, the Environmental Mapping and Analysis Program (En-MAP) [11], wich will generate a huge amount of hyperspectral data. Content Base Image Retrieval (CBIR) systems are relevant to the geosciences because they provide automated tools to explore and understand the contents of large and highly complex images [17,9,7]. There have been several efforts along this decade to develop CBIR tools for remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Hyperspectral Content-Based Information Retrieval (RS-CBIR) System Upon Scarce Data

Veganzones

Graña

2011

Advances in Intelligent and Soft Computing

View full text Add to dashboard Cite

V alidation of Remote Sensing Content-Based Information Retrieval (RS-CBIR) systems requires innovative strategies due to the scarcity of labelled data. CBIR systems validation by means of precision/recall measures based on either user feedback or a-priori known categories, are hard to apply to RS-CBIR systems. We propose to apply a data-driven (unsupervised) quality assessment strategy analogous to the DAMA strategy applied for the validation of classication methods used in thematic mapping. The strategy is intended for quality assessment when little or no ground truth is available. The proposed strategy deals with the RS-CBIR validation problem by giving a quantitative and qualitative evidence of the relative (subjective) quality of RS-CBIR systems without need of a-priori knowledge. We apply the proposed strategy to validate a Hyperspectral CBIR system.

show abstract

Human-centered concepts for exploration and understanding of Earth observation images

Cited by 66 publications

References 14 publications

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis

High-Resolution Satellite Imagery Changes Detection using Agglomerative Fuzzy K-Means Clustering Algorithm

Validation of a Hyperspectral Content-Based Information Retrieval (RS-CBIR) System Upon Scarce Data

Contact Info

Product

Resources

About