A Content-Based Remote Sensing Image Change Information Retrieval Model

Ma, Caihong; Xia, Wei; Fu, Chen; Dai, Qin; Jiang, Liyuan; Duan, Jianbo; Liu, Wei

doi:10.20944/preprints201708.0102.v1

Cited by 5 publications

(2 citation statements)

References 14 publications

(25 reference statements)

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“…Information retrieval refers to the process and technology of organizing information in a certain way, through which the relevant information according to the needs of the users can be located. e four most traditional categories of information retrieval models [4] are Boolean, vector space, probability, and language models. In modern Internet, the basic unit for the amount of information has risen to 10 billion, including texts, images, and videos, consequently making retrieval more difficult.…”

Section: Development Of Cross-media Retrieval Technology 121 Overview Of Information Retrieval Modelsmentioning

confidence: 99%

A Cross-Media Retrieval Method Based on Semisupervised Learning and Alternate Optimization

Zhu

Yang

et al. 2021

Mobile Information Systems

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With the continuous advancement in Internet technology, we are gradually stepping into an era of big data where a large amount of multimedia data is produced every day at any given time. In order to properly utilize these data, the research on big data is also constantly evolving. Cross-media retrieval is a prime example, aiming at retrieving various forms of data, for example, text, image, audio, video, and other forms. The most difficult task for cross-media retrieval lies in the potential correlation between different modalities data and how to overcome the semantic gap. This paper proposes a cross-media retrieval method based on semisupervised learning and alternate optimization (SMDCR) to overcome the abovementioned difficulties, thereby improving the retrieval accuracy. The main advantage of this method is to make full use of the degree of correlation between the semantic information of the labeled data and unlabeled data. Simultaneously, we combine the linear regression term, correlation analysis term, and feature selection term into a joint cross-media learning framework. Furthermore, the projection matrices are trained with the alternate optimization method. Finally, experimental results on two public datasets demonstrate the effectiveness of the proposed method.

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Section: Development Of Cross-media Retrieval Technology 121 Overview Of Information Retrieval Modelsmentioning

confidence: 99%

A Cross-Media Retrieval Method Based on Semisupervised Learning and Alternate Optimization

Zhu

Yang

et al. 2021

Mobile Information Systems

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“…It could reflect the difference in the correlative attribute characteristics of different sources of remote sensing data, to some degree (Tsai et al, 2008;Hu et al, 2015). Additionally,, as the quantity and categories of remote sensing images that are applied to various application areas gradually increases, the image entropy is also regarded as a very valid means to improve the utilization efficiency and availability of obtained diversified remote sensing data (Ma et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

An Approach for Evaluating the Information Content of Remote Sensing Images

Fang¹,

Zhou²

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Due to being affected by the rapid development of open science and the increasing popularity of mobile devices (e.g., smartphones), remote sensing data as frequently used data sources are broadly applied to our daily life. At the same time, remote sensing data collection also presents a trend of popularization. To improve the utilization efficiency and availability of the obtained diversified remote sensing data, we propose a novel evaluation method based on information theory and scatterplot mapping model, i.e., geometrical mapping entropy (GME). The goal is to construct a unified model of measurement to be much more effectively and accurately evaluate the information content and quality of remotely sensed imagery. Different experimental data are used to verify the performance of the proposed method, i.e., a group of the dataset that contains different four types of images; the other group of image data contains the images with different modalities and different imaging times (2016–05, 2017–08, 2018–04, and 2018–06). Experimental results indicate that the proposed approach can better characterize the spectrum features and spatial structural features contained in images and visual perception information. Additionally, it can also reflect the difference in the quality of different modality images, especially the effect for the images that contain clouds or poor lighting conditions, is better.

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A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery

Fang

Zhou

Zhang

2019

IJGI

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Considering the multiscale characteristics of the human visual system and any natural scene, the spatial autocorrelation of remotely sensed imagery, and the multilevel spatial structure of ground targets in remote sensing images, an information-measurement approach based on a single-level geometrical mapping model can only reflect partial feature information at a single level (e.g., global statistical information and local spatial distribution information). The single mapping model cannot validly characterize the information of the multilevel and multiscale features of the spatial structures inherent in remotely sensed images. Additionally, the validity, practicability, and application range of the results of single-level mapping models are greatly limited in practical applications. In this paper, we present the multilevel geometrical mapping entropy (MGME) model to evaluate the information content of related attribute characteristics contained in remotely sensed images. Subsequently, experimental images with different types of objects, including reservoir area, farmland, water area (i.e., water and trees), and mountain area, were used to validate the performance of the proposed method. Experimental results show that the proposed method can not only reflect the difference in the information of images in terms of spectrum features, spatial structural features, and visual perception but also eliminates the inadequacy of a single-level mapping model. That is, the multilevel mapping strategy is feasible and valid. Additionally, the vector set of the MGME method and its standard deviation (Std) value can be used to further explore and study the spatial dependence of ground scenes and the difference in the spatial structural characteristics of different objects.

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A Content-Based Remote Sensing Image Change Information Retrieval Model

Cited by 5 publications

References 14 publications

A Cross-Media Retrieval Method Based on Semisupervised Learning and Alternate Optimization

A Cross-Media Retrieval Method Based on Semisupervised Learning and Alternate Optimization

An Approach for Evaluating the Information Content of Remote Sensing Images

A Multilevel Mapping Strategy to Calculate the Information Content of Remotely Sensed Imagery

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