التفاصيل البيبلوغرافية
| العنوان: |
CBIR-SAR System Using Stochastic Distance. |
| المؤلفون: |
Sousa AD; Informatics Systems, Federal University of Piaui, Picos 64607-825, Piaui, Brazil.; Teleinformatics Engineering, Federal University of Ceara, Fortaleza 60455-970, Ceara, Brazil., Silva PHDS; Computer Engineering, Federal University of Ceara, Fortaleza 60455-970, Ceara, Brazil., Silva RRV; Informatics Systems, Federal University of Piaui, Picos 64607-825, Piaui, Brazil., Rodrigues FAÀ; Computational Mathematics, Federal University of Cariri, Juazeiro do Norte 63048-080, Ceara, Brazil., Medeiros FNS; Teleinformatics Engineering, Federal University of Ceara, Fortaleza 60455-970, Ceara, Brazil. |
| المصدر: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Jul 01; Vol. 23 (13). Date of Electronic Publication: 2023 Jul 01. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: |
Original Publication: Basel, Switzerland : MDPI, c2000- |
| مواضيع طبية MeSH: |
Radar* , Forests*, Databases, Factual |
| مستخلص: |
This article proposes a system for Content-Based Image Retrieval (CBIR) using stochastic distance for Synthetic-Aperture Radar (SAR) images. The methodology consists of three essential steps for image retrieval. First, it estimates the roughness (α^) and scale (γ^) parameters of the GI0 distribution that models SAR data in intensity. The parameters of the model were estimated using the Maximum Likelihood Estimation and the fast approach of the Log-Cumulants method. Second, using the triangular distance, CBIR-SAR evaluates the similarity between a query image and images in the database. The stochastic distance can identify the most similar regions according to the image features, which are the estimated parameters of the data model. Third, the performance of our proposal was evaluated by applying the Mean Average Precision (MAP) measure and considering clippings from three radar sensors, i.e., UAVSAR, OrbiSaR-2, and ALOS PALSAR. The CBIR-SAR results for synthetic images achieved the highest MAP value, retrieving extremely heterogeneous regions. Regarding the real SAR images, CBIR-SAR achieved MAP values above 0.833 for all polarization channels for image samples of forest (UAVSAR) and urban areas (ORBISAR). Our results confirmed that the proposed method is sensitive to the degree of texture, and hence, it relies on good estimates. They are inputs to the stochastic distance for effective image retrieval. |
| References: |
IEEE Trans Pattern Anal Mach Intell. 2012 Oct;34(10):2046-57. (PMID: 22899373) |
| فهرسة مساهمة: |
Keywords: CBIR; SAR; fast log-cumulants method; maximum likelihood estimation; stochastic distance |
| تواريخ الأحداث: |
Date Created: 20230714 Date Completed: 20230717 Latest Revision: 20230718 |
| رمز التحديث: |
20240628 |
| مُعرف محوري في PubMed: |
PMC10347088 |
| DOI: |
10.3390/s23136080 |
| PMID: |
37447929 |
| قاعدة البيانات: |
MEDLINE |
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