Integration of remotely sensed spatial and spectral information for change detection using FAHP

Eisavi, Vahid; Homayouni, Saeid; Karami, Jalal

doi:10.17099/jffiu.90466

Cited by 4 publications

(4 citation statements)

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“…The consensus was then translated into a hierarchy framework of pre-reading strategies (Figure 2) to help implement these strategies according to their degree of importance in the English reading classroom. At the same time, since the fuzziness of human thought can be further expressed, the Fuzzy Delphi Method (FDM) and Fuzzy Analytic Hierarchy Process (FAHP) were employed to increase the accuracy of the findings (Eisavi, Homayouni & Karami [25]). …”

Section: Methodsmentioning

confidence: 99%

Implementing Pre-Reading Strategies to Improve Struggling ESL Learners’ Interest and Comprehension in English Reading Lessons

Thuraisingam¹

2017

IJECS

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This paper presents the implementation of pre-reading strategies to facilitate reading skills and increase interest in reading lessons among struggling ESL primary school pupils using the Fuzzy Delphi Method (FDM) and Fuzzy Analytic Hierarchy Process (FAHP) which is a multiple-attribute decision process that is normally made by a review committee with experts from academia. It also aims at providing a systematic approach to selecting appropriate pre-reading strategies to improve pupils' interest and comprehension in English reading lessons. In this study, six English Language teachers from Kuala Lumpur participated as experts to answer a questionnaire in the Fuzzy Delphi method to determine pre-reading strategies that facilitate reading among struggling Year Four pupils. A total of 250 Year Four pupils participated in a comprehension pretest from which thirty-five struggling Year Four pupils were selected. Through the Fuzzy Delphi Method (FDM) and the Fuzzy Analytic Hierarchy Process (FAHP), three key pre-reading strategies were selected and sequenced according to their degree of importance as selected by the experts. The percentage of consensus achieved was 85% and the fuzzy hierarchy for pre-reading strategies was determined and ranked according to its importance namely activating prior knowledge, teaching explicit vocabulary, and conducting role-play. These strategies were implemented by the expert teachers during two English reading Research Cycles after which a post-test was administered. The post-test and checklists of observation on expert teachers indicated that the chosen pre-reading strategies had a significant effect in increasing pupils' interest and comprehension in reading.

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Section: Methodsmentioning

confidence: 99%

Implementing Pre-Reading Strategies to Improve Struggling ESL Learners’ Interest and Comprehension in English Reading Lessons

Thuraisingam¹

2017

IJECS

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“…Land surface change monitoring is an important application of remote sensing image change detection [1], extensively utilized across various domains, including land management [2], disaster evaluation [3], agricultural analysis [4], landslide detection [5], and ecosystem surveillance [6]. However, the increasing pace of urbanization and shifts in ecological environments have significantly increased the complexity of land surfaces.…”

Section: Introductionmentioning

confidence: 99%

AMFNet: Attention-Guided Multi-Scale Fusion Network for Bi-Temporal Change Detection in Remote Sensing Images

Zhan,

Ren,

Xia

et al. 2024

Remote Sensing

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Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model’s understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities.

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“…Pixels in unchanged areas are labeled 0, while pixels in changed areas are labeled 1. Remote-sensing image change-detection technology has been widely used in urban development planning [1], agricultural surveys [2,3], land management [4,5], and natural disaster assessment [6,7].…”

Section: Introductionmentioning

confidence: 99%

MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure

et al. 2023

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The change-detection task is essentially a binary semantic segmentation task of changing and invariant regions. However, this is much more difficult than simple binary tasks, as the changing areas typically include multiple terrains such as factories, farmland, roads, buildings, and mining areas. This requires the ability of the network to extract features. To this end, we propose a multi-branch collaborative change-detection network based on Siamese structure (MHCNet). In the model, three branches, the difference branch, global branch, and similar branch, are constructed to refine and extract semantic information from remote-sensing images. Four modules, a cross-scale feature-attention module (CSAM), global semantic filtering module (GSFM), double-branch information-fusion module (DBIFM), and similarity-enhancement module (SEM), are proposed to assist the three branches to extract semantic information better. The CSFM module is used to extract the semantic information related to the change in the remote-sensing image from the difference branch, the GSFM module is used to filter the rich semantic information in the remote-sensing image, and the DBIFM module is used to fuse the semantic information extracted from the difference branch and the global branch. Finally, the SEM module uses the similar information extracted with the similar branch to correct the details of the feature map in the feature-recovery stage.

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Integration of remotely sensed spatial and spectral information for change detection using FAHP

Cited by 4 publications

References 0 publications

Implementing Pre-Reading Strategies to Improve Struggling ESL Learners’ Interest and Comprehension in English Reading Lessons

Implementing Pre-Reading Strategies to Improve Struggling ESL Learners’ Interest and Comprehension in English Reading Lessons

AMFNet: Attention-Guided Multi-Scale Fusion Network for Bi-Temporal Change Detection in Remote Sensing Images

MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure

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