Mining of Remote Sensing Image Archives using Spatial Relationship Histograms

Kalaycilar, Firat; Kale, Asli; Zamalieva, Daniya; Aksoy, Selim

doi:10.1109/igarss.2008.4779416

Cited by 9 publications

(5 citation statements)

References 9 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this method also, the invariant spatial signatures based on histogram of forces are extracted between pairs of spatial objects. Another feature-based approach based on spatial relation histogram is proposed in [28], which generates histogram for the binary, ternary, and high-level spatial relationships in a scene. In this method, all the possible spatial relationships among all possible LULC classes are first generated and from the ARG of an RS image scene, histograms are computed by counting the occurrence of these possible spatial relations in the scene.…”

Section: A State-of-the-art In Modeling Spatial Relations In Rs Datamentioning

confidence: 99%

Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Kurte

Durbha

King

et al. 2017

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

View full text Add to dashboard Cite

Recent developments in sensor technology are contributing toward the tremendous growth of remote sensing (RS) archives (currently, at the petabyte scale). However, this data largely remain unexploited due to the current limitations in the data discovery, querying, and retrieval capabilities. This issue becomes exacerbated in disaster situations, where there is a need for rapid processing and retrieval of the affected areas. Furthermore, the retrieval of images based on the spatial configurations of affected regions [land use/cover (LULC) classes] in an image is important in disaster situations such as floods and earthquakes. The majority of existing Earth observation (EO) image information mining (IIM) systems does not consider the spatial relations among image regions during image retrieval (aka spatial semantic gap). In this work, we have specifically addressed two issues, i.e., explicit modeling of topological and directional relationships between image regions, and development of a resource description framework (RDF)-based spatial semantic graphs (SSGs). This enables more intuitive querying and reasoning on the archived data. A spatial IIM (SIIM) framework isproposed, which integrates a logic-based reasoning mechanism to extract the hidden spatial relationships (both topological and directional) and enables image retrieval based on spatial relationships. The system is tested using several spatial relations-based queries on the RS image repository of flood-affected areas to check its applicability in post flood scenario. Precision, recall, and F-measure metrics were used to evaluate the performance of the SIIM system, which showed good potential for spatial relations-based image retrieval.Index Terms-Description logic (DL), direction relations, image retrieval, linked data, reasoning, region connection calculus (RCC-8), resource description framework (RDF), semantic web rules language (SWRL), SPARQL, spatial image information mining (SIIM), spatial relations, topological relations, Web ontology language (OWL).

show abstract

Section: A State-of-the-art In Modeling Spatial Relations In Rs Datamentioning

confidence: 99%

Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Kurte

Durbha

King

et al. 2017

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

View full text Add to dashboard Cite

show abstract

“…In [40], the latent Dirichlet allocation model was utilized to map low-level features of clusters and segment the high-level map labels for remote sensing image annotation and mapping. Finally, Kalaycilar et al [41] computed the topological and distance-based spatial relationships between the regions. The spatial relationship histograms were constructed by classifying these regions in an image.…”

Section: Image Retrievalmentioning

confidence: 99%

A Three-Layered Graph-Based Learning Approach for Remote Sensing Image Retrieval

Wang

Zhang

Tong

et al. 2016

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

With the emergence of huge volumes of highresolution remote sensing images produced by all sorts of satellites and airborne sensors, processing and analysis of these images require effective retrieval techniques. To alleviate the dramatic variation of the retrieval accuracy among queries caused by the single image feature algorithms, we developed a novel graph-based learning method for effectively retrieving remote sensing images. The method utilizes a three-layer framework that integrates the strengths of query expansion and fusion of holistic and local features. In the first layer, two retrieval image sets are obtained by, respectively, using the retrieval methods based on holistic and local features, and the top-ranked and common images from both of the top candidate lists subsequently form graph anchors. In the second layer, the graph anchors as an expansion query retrieve six image sets from the image database using each individual feature. In the third layer, the images in the six image sets are evaluated for generating positive and negative data, and SimpleMKL is applied to learn suitable query-dependent fusion weights for achieving the final image retrieval result. Extensive experiments were performed on the UC Merced Land Use-Land Cover data set. The source code has been available at our website. Compared with other related methods, the retrieval precision is significantly enhanced without sacrificing the scalability of our approach.

show abstract

“…In the second category, Kalaycilar et al [20] used spatial relationship histograms to represent an image and the modeled image content by attributed relation graphs for retrieval. Espinoza-Molina et al [7] proposed an EO image retrieval system based on enriched metadata, semantic annotations, and image content.…”

Section: E Arth Observation (Eo) Is the Integration Of Informationmentioning

confidence: 99%

SAR Images Retrieval Based on Semantic Classification and Region-Based Similarity Measure for Earth Observation

Jiao

Tang

Hou

et al. 2015

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

View full text Add to dashboard Cite

Based on the semantic categorization and regionbased similarity measure, a novel synthetic aperture radar (SAR) image retrieval method is proposed in this paper, which is inspired by the existing content-based image retrieval (CBIR) techniques and is oriented toward the Earth observation (EO). First, due to the large sizes of SAR images, new method semantically classifies the land covers in the patch level rather than the pixel level by the classic semisupervised learning (SSL), which could reduce the workload of selecting the representative image patch and decrease the searching space in the similarity calculation component. Furthermore, to overcome the inevitable classification error, our method provides an error recovery scheme, preventing the errors produced in categorization to contaminate the retrieval results. Third, the similarity between two patches is calculated by the improved integrated region matching (IIRM) measure based on the region-based similarity measure, which fails to meet the expectation in SAR images. The proposed method can be embedded into any EO mining systems to help them complete the EO missions. After comparing the method presented in this paper to others, it is evident that our method performs more effectively than others from the CBIR aspect.Index Terms-Content-based image retrieval (CBIR), Earth observation (EO), improved integrated region matching (IIRM) measure, synthetic aperture radar (SAR) image retrieval, semantic categorization.

show abstract

Mining of Remote Sensing Image Archives using Spatial Relationship Histograms

Cited by 9 publications

References 9 publications

Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

Semantics-Enabled Framework for Spatial Image Information Mining of Linked Earth Observation Data

A Three-Layered Graph-Based Learning Approach for Remote Sensing Image Retrieval

SAR Images Retrieval Based on Semantic Classification and Region-Based Similarity Measure for Earth Observation

Contact Info

Product

Resources

About