Entropy-Based Block Processing for Satellite Image Registration

Lee, Ik-Hyun; Seo, Doochun; Choi, Tae‐Sun

doi:10.3390/e14122397

Cited by 8 publications

(9 citation statements)

References 14 publications

(13 reference statements)

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“…Table 1 shows the number of initial CPs, corresponding CPs and refined CPs for different feature-based and area-based methods. The refined CPs were obtained using random sample consensus (RANSAC) [2]. In the table, it is clear that feature-based methods (SIFT and SURF) extracted more features than area-based methods (SSD, NCC, and FZNCC).…”

Section: Cpr =mentioning

confidence: 99%

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Well-Distributed Feature Extraction for Image Registration Using Histogram Matching

Mahmood

Lee

2019

Applied Sciences

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Image registration is a spatial alignment of corresponding images of the same scene acquired from different views, sensors, and time intervals. Especially, satellite image registration is a challenging task due to the high resolution of images. In addition, demands for high resolution satellite imagery are increased for more detailed and precise information in land planning, urban planning, and Earth observation. Commonly, feature-based methods are applied for image registration. In these methods, first control or key points are detected using feature detector such as scale-invariant feature transform (SIFT). The numbers and the distribution of these control points are important for the remaining steps of registration. These methods provide reasonable performance; however, they suffer from high computational cost and irregular distribution of control points. To overcome these limitations, we propose an area-based registration method using histogram matching and zero mean normalized cross-correlation (ZNCC). In multi-spectral satellite images, first, different spectral responses are adjusted by using histogram matching. Then, ZNCC is utilized to extract well-distributed control points. In addition, fast Fourier transform (FFT) and block-wise processing are applied to reduce the computational cost. The proposed method is evaluated through various input datasets. The results demonstrate its efficacy and accuracy in image registration.

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Section: Cpr =mentioning

confidence: 99%

“…High-resolution images may occupy over a hundred megabytes with several spectral bands or gigapixels with hyper-spectral bands. Therefore, processing these images with large sizes is difficult due to the limited resources and memory [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Well-Distributed Feature Extraction for Image Registration Using Histogram Matching

Mahmood

Lee

2019

Applied Sciences

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“…However, the current spatial objects stem from different domains, and it is difficult to determine the weight by experts in a specific domain. Therefore, under the condition of a lack of existing knowledge, we adopt an objective probability method to quantify the related weight by using axiomatic characterizations of information entropy, according to Shanonn [11,[23][24][25]. b .…”

Section: The Entropy-based Weighted Concept Latticementioning

confidence: 99%

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

Zhu

2013

Entropy

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Abstract:To deal with the complexities associated with the rapid growth in a merged concept lattice, a formal method based on an entropy-based weighted concept lattice (EWCL) is proposed as a mechanism for merging multi-source geographic ontologies (geo-ontologies). First, formal concept analysis (FCA) is used to formalize different term-based representations in relation to the geographic domain, and to construct a merged formal context. Second, a weighted concept lattice (WCL) is applied to reduce the merged concept lattice, based on information entropy and a deviance analysis. The entropy of the attribute set is exploited to acquire the intent weight value, and the standard deviation contributes to computing the intent importance deviance value, according to the user preferences and interests. Some nodes of the merged concept lattice are then removed if their intent weights are lower than the intent importance thresholds specified by the user. Finally, experiments were conducted by combining fundamental geographic information data and spatial data in the hydraulic engineering domain from China. The results indicate that the proposed method is feasible and valid for reducing the complexities associated with the merging of geo-ontologies. Although there are still some problems in the application, the manuscript offers a new approach for the merging of geo-ontologies.

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“…In Section 4, we assign the weight to each property of the decision table ( [10,[20][21][22], to the foundation of the importance of the inclusion degree we introduce information entropy to characterize the combined weights of various attributes that belong to the same property in the decision table.…”

Section: Construction Of a Combined Weighted Concept Lattice With Incmentioning

confidence: 99%

“…How to identify the importance weight of each attribute has become an important and valuable task. Information entropy is introduced to assign a weight to attributes in some cases [20][21][22], the basis of which was used by Li et al [10] to design a workflow to build an entropy-based, weighted concept lattice. Weight based on information entropy considers the diversity of the attributes, but ignores their relativity.…”

Section: An Attribute Importance Definition Based On the Inclusion Dementioning

confidence: 99%

A Concept Lattice for Semantic Integration of Geo-Ontologies Based on Weight of Inclusion Degree Importance and Information Entropy

Xiao

Zhu

2016

Entropy

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Constructing a merged concept lattice with formal concept analysis (FCA) is an important research direction in the field of integrating multi-source geo-ontologies. Extracting essential geographical properties and reducing the concept lattice are two key points of previous research. A formal integration method is proposed to address the challenges in these two areas. We first extract essential properties from multi-source geo-ontologies and use FCA to build a merged formal context. Second, the combined importance weight of each single attribute of the formal context is calculated by introducing the inclusion degree importance from rough set theory and information entropy; then a weighted formal context is built from the merged formal context. Third, a combined weighted concept lattice is established from the weighted formal context with FCA and the importance weight value of every concept is defined as the sum of weight of attributes belonging to the concept's intent. Finally, semantic granularity of concept is defined by its importance weight; we, then gradually reduce the weighted concept lattice by setting up diminishing threshold of semantic granularity. Additionally, all of those reduced lattices are organized into a regular hierarchy structure based on the threshold of semantic granularity. A workflow is designed to demonstrate this procedure. A case study is conducted to show feasibility and validity of this method and the procedure to integrate multi-source geo-ontologies.

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Entropy-Based Block Processing for Satellite Image Registration

Cited by 8 publications

References 14 publications

Well-Distributed Feature Extraction for Image Registration Using Histogram Matching

Well-Distributed Feature Extraction for Image Registration Using Histogram Matching

An Entropy-Based Weighted Concept Lattice for Merging Multi-Source Geo-Ontologies

A Concept Lattice for Semantic Integration of Geo-Ontologies Based on Weight of Inclusion Degree Importance and Information Entropy

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