.png){kind=icon}
返回
CBIR-SAR System Using Stochastic Distance
.png){kind=icon}
OA
AI
DOI：10.3390/s23136080.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 ((alpha) over cap) and scale ((gamma) over cap) 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.
Keyword:
SAR
CBIR
stochastic distance
fast log-cumulants method
maximum likelihood estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Lactation and recovery period in an inbred mouse strain: coefficients of variation and coefficients of correlation between body weight and body composition, adipocyte size and organ weights of liver and heart近交小鼠品系中的泌乳与恢复期：体重与体成分、脂肪细胞大小以及肝脏和心脏器官重量的变异系数和相关性系数
PREAI
分享收藏
Interactive learning and probabilistic retrieval in remote sensing image archives遥感影像档案中的交互式学习与概率检索
PREAI
分享收藏High-Speed and Low-Energy Resistive Switching with Two-Dimensional Cobalt Phosphorus Trisulfide for Efficient Neuromorphic Computing二维钴磷化三硫用于高效神经形态计算的高速低能阻变开关.png)
ACS NANO
ACS NANOIF16
PREAI
分享收藏分享收藏
查看更多内容