A Knowledge-Based System for Context Dependent Evaluation of Remote Sensing Data

Buckner, John C.; Pahl, M.; Stahlhut, O.; Liedtke, Claus-E.

doi:10.1007/3-540-45783-6_8

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…In order to solve the aforementioned problems and to model the scenes and backgrounds, namely context constraints, researchers have made considerable efforts to advance the detection performance in complex contexts of remote sensing images. Among the traditional target detection methods, some context knowledgebased algorithms are proposed in context-aware detectors, which is an offshoot of knowledge-based object detection [10,11,12,13]. The most frequently used context knowledge lies in how targets in images interact with adjacent regions, i.e., objectbackground relationships.…”

Section: Figure 1 Representative Detection Problems In Remote Sensing...mentioning

confidence: 99%

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Ding,

Lyu,

Yan

et al. 2023

cai

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Remote sensing target detection has been widely used in industries. In various application scenarios, complicated contexts may inhibit target identification and reduce detection accuracy, especially in multi-target detection tasks. In this paper, a new remote sensing target detection method based on structural reasoning is proposed to improve target detection performance by integrating inter-object relationships and scene information. Based on inter-object information, a relation structure graph is designed to reduce errors and missed targets. To establish * Corresponding author Remote Sensing Target Detection by Scene and Inter-Object Relations 1405 contextual constraints, semantic is used as a prior information for Bayesian criterion based on scene information. Experiments conducted on HRRSD dataset show that the average accuracy of the proposed method is 10.7 % higher than the state-of-the-art algorithms. The experimental results confirm that the proposed algorithm can achieve significant improvements and adapt to complex scenes in remote sensing by mining contextual information at both feature and semantic levels.

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Section: Figure 1 Representative Detection Problems In Remote Sensing...mentioning

confidence: 99%

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Ding,

Lyu,

Yan

et al. 2023

cai

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“…Previous work has explored various ways of representing information in knowledge bases (Bordes et al, 2011) and improving these representations (Chen et al, 2013). Knowledge bases have been leveraged to improve performance on various tasks, from coreference resolution (Ng and Cardie, 2002) and question answering (Zheng, 2003;Bao et al, 2014;Cui et al, 2017; to signal processing (Bückner et al, 2002). Various works convert text into Abstract Meaning Representations (Liu et al, 2018a) for domains such as news (Vossen et al, 2015;Rospocher et al, 2016) and link nodes to large knowledge bases such as DBPedia (Auer et al, 2007).…”

Section: Using Knowledge Basesmentioning

confidence: 99%

Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs

Fan¹,

Gardent²,

Braud³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Query-based open-domain NLP tasks require information synthesis from long and diverse web results.Current approaches extractively select portions of web text as input to Sequence-to-Sequence models using methods such as TF-IDF ranking. We propose constructing a local graph structured knowledge base for each query, which compresses the web search information and reduces redundancy. We show that by linearizing the graph into a structured input sequence, models can encode the graph representations within a standard Sequence-to-Sequence setting. For two generative tasks with very long text input, long-form question answering and multidocument summarization, feeding graph representations as input can achieve better performance than using retrieved text portions. Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading wikipedia to answer opendomain questions. In ACL. Danqi Chen, Richard Socher, Christopher D Manning, and Andrew Y Ng. 2013. Learning new facts from knowledge bases with neural tensor networks and semantic word vectors. arXiv preprint arXiv:1301.3618. Christopher Clark and Matt Gardner. 2017. Simple and effective multi-paragraph reading comprehension. arXiv preprint arXiv:1710.10723. Kevin Clark and Christopher D Manning. 2016a. Deep reinforcement learning for mention-ranking coreference models. arXiv preprint arXiv:1609.08667. Kevin Clark and Christopher D Manning. 2016b. Improving coreference resolution by learning entitylevel distributed representations. arXiv preprint arXiv:1606.01323. . 2017. Kbqa: learning question answering over qa corpora and knowledge bases. Proceedings of the VLDB Endowment, 10(5):565-576. . 2019. Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860.

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“…The method is based on a fuzzy clustering algorithm, partially supervised by information on the shape of the object and textual labels related to semantic categories. In the remote sensing field, the Institut für Theoretische Nachrichtentechnik und Informationsverarbeitung 1 made a lot of effort since many years for incorporating a priori knowledge into the image interpretation process [39,7,8]. Their GeoAIDA system uses a semantic net to model a priori knowledge on the objects in the studied scene.…”

Section: Knowledge-based Systems For Image Analysismentioning

confidence: 99%

Knowledge-based region labeling for remote sensing image interpretation

Forestier

Puissant

Wemmert

et al. 2012

Computers, Environment and Urban Systems

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The increasing availability of High Spatial Resolution (HSR) satellite images is an opportunity to characterize and identify urban objects. Thus, the augmentation of the precision led to a need of new image analysis methods using region-based (or object-based) approaches. In this field, an important challenge is the use of domain knowledge for automatic urban objects identification, and a major issue is the formalization and exploitation of this knowledge. In this paper, we present the building steps of a knowledge-base of urban objects allowing to perform the interpretation of HSR images in order to help urban planners to automatically map the territory. The knowledge-base is used to assign segmented regions (i.e. extracted from the images) into semantic objects (i.e. concepts of the knowledge-base). A matching process between the regions and the concepts of the knowledge-base is proposed, allowing to bridge the semantic gap between the images content and the interpretation. The method is validated on Quickbird images of the urban areas of Strasbourg and Marseille (France). The results highlight the capacity of the method to automatically identify urban objects using the domain knowledge.

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A Knowledge-Based System for Context Dependent Evaluation of Remote Sensing Data

Cited by 12 publications

References 6 publications

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs

Knowledge-based region labeling for remote sensing image interpretation

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