System refinement for content based satellite image retrieval

Laban, Noureldin; El-Saban, Motaz; Nasr, Ayman H.; Onsi, Hoda M.

doi:10.1016/j.ejrs.2012.05.001

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…The retrieval process, as shown in figure 6, has two sub stages as mentioned in [1], the candidates selection stage and the refinement stage.…”

Section: B Retrieval Processmentioning

confidence: 99%

“…Second stage is cloud detection and retrieval stage in each satellite scene, which determines where the clouds in this scene and their percentage are. We have used two strategies in our system [1]. First one is query by polygon strategy where we build our signature database using cloud polygons instead of rectangular shapes.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…3, No. 12, 2012 214 | P a g e www.ijacsa.thesai.org According to the two stages hierarchy used in [1] for the retrieval process, we have rebuilt the system. Instead of starting with features databases and get query features for each semantic, we have reverse the order which begins with building cloud signature database then the input scene is treated as query image.…”

Section: B Cloud Detection and Retrieval Stagementioning

confidence: 99%

“…There is huge image content appearing every second through multiple competing satellite systems [1]. Manual interaction with this large volume of data is becoming more and more inappropriate, which creates an urgent need for automatic treatment to store, organize and retrieve this content [2].…”

Section: Introductionmentioning

confidence: 99%

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Spatial Cloud Detection and Retrieval System for Satellite Images

Laban¹,

Nasr²,

El-Saban³

et al. 2012

IJACSA

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Abstract-In last the decade we witnessed a large increase in data generated by earth observing satellites. Hence, intelligent processing of the huge amount of data received by hundreds of earth receiving stations, with specific satellite image oriented approaches, presents itself as a pressing need. One of the most important steps in earlier stages of satellite image processing is cloud detection. Satellite images having a large percentage of cloud cannot be used in further analysis. While there are many approaches that deal with different semantic meaning, there are rarely approaches that deal specifically with cloud detection and retrieval. In this paper we introduce a novel approach that spatially detect and retrieve clouds in satellite images using their unique properties .Our approach is developed as spatial cloud detection and retrieval system (SCDRS) that introduce a complete framework for specific semantic retrieval system. It uses a Query by polygon (QBP) paradigm for the content of interest instead of using the more conventional rectangular query by image approach. First, we extract features from the satellite images using multiple tile sizes using spatial and textural properties of cloud regions. Second, we retrieve our tiles using a parametric statistical approach within a multilevel refinement process. Our approach has been experimentally validated against the conventional ones yielding enhanced precision and recall rates in the same time it gives more precise detection of cloud coverage regions.

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“…The retrieval process, as shown in figure 6, has two sub stages as mentioned in [1], the candidates selection stage and the refinement stage.…”

Section: B Retrieval Processmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

Section: B Cloud Detection and Retrieval Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Cloud Detection and Retrieval System for Satellite Images

Laban¹,

Nasr²,

El-Saban³

et al. 2012

IJACSA

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“…NourElDin Laban et al (2012) have made use of two dissimilar types of features, viz. Color Histograms (CH) and textures in wavelet domain for every band of the multispectral image.…”

mentioning

confidence: 99%

Multiscale Texture Analysis and Color Coherence Vector Based Feature Descriptor for Multispectral Image Retrieval

Sudheer¹,

Krishnan²

2019

Adv. sci. technol. eng. syst. j.

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Content Based Image Retrieval (CBIR) for remote sensing image data is a tedious process due to high resolution and complexity of image interpretation. Development of feature extraction technique is a major portion to represent the image content in an optimal way. In this paper, we propose a feature descriptor which combines the color coherent pixel information and GLCM texture features in multi scale domain. Curvelet transform is used to decompose the image into coarse and detail coefficients. Then Gabor magnitude is computed for each coefficient to improve the directional information. GLCM texture features are extracted from the Gabor magnitude response. The novel feature set by combining the CCV and GLCM using curvelet and Gabor filter is developed. Mahalanobis distance measure is used to find the similarity between query and feature database. Average Normalized Modified Retrieval Rate (ANMRR) is computed to evaluate the performance with the state of art methods.

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A Review on Recent Advances in Remote Sensing Image Retrieval Techniques

Sudha

Aji

2019

J Indian Soc Remote Sens

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System refinement for content based satellite image retrieval

Cited by 10 publications

References 13 publications

Spatial Cloud Detection and Retrieval System for Satellite Images

Spatial Cloud Detection and Retrieval System for Satellite Images

Multiscale Texture Analysis and Color Coherence Vector Based Feature Descriptor for Multispectral Image Retrieval

A Review on Recent Advances in Remote Sensing Image Retrieval Techniques

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