Change Detection in Landsat Images Using Unsupervised Learning and RBF-Based Clustering

Gupta, Neha; Ari, Samit; Panigrahi, Narayan

doi:10.1109/tetci.2019.2932087

Cited by 19 publications

(6 citation statements)

References 45 publications

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“…The elevation angle and pixel location in the captured image are different for different year's captured images. Therefore, mapping the subset of consecutive years glacial lake region from the captured image is difficult with visual interpretation 9 . However, the subset of consecutive years images are mapped with geographic location's coordinates of a captured image, which are identified with the help of coordinate reference system like, UTM and WGS.…”

Section: Methodology: the Proposed Glesi Systemmentioning

confidence: 99%

“…Therefore, mapping the subset of consecutive years glacial lake region from the captured image is difficult with visual interpretation. 9 However, the subset of consecutive years images are mapped with geographic location's coordinates of a captured image, which are identified with the help of coordinate reference system like, UTM and WGS. Next, enhanced water features are obtained using the MNDWI technique by calculating with green and SWIR band spectral reflectance in (1).…”

Section: Preprocessingmentioning

confidence: 99%

“…The Landsat satellite data provides a permanent and authentic record of a particular area at any given time, which can be reused for verification and reassessment. The Landsat 8 satellite, with an elevation of 705 km and covers 185

\times

180 km

{}^2

is identified with the help of coordinate reference systems like universal transverse mercator (UTM) and world geodetic system (WGS) 3,9 . This satellite provides eleven bands of multispectral data

{I}_1,{I}_2,\dots, {I}_{11}

.…”

Section: Introductionmentioning

confidence: 99%

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GLeSI: A system for extraction of glacial lakes using satellite imagery

Thati

Ari

2022

Concurrency and Computation

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Summary For outburst susceptibility assessment of glacial lakes, manual field survey is a time‐consuming, expensive, and very tedious process. Therefore, an automatic and reliable system is required for efficient extraction of glacial lake's from satellite imagery. Low spectral contrast and heterogeneous backgrounds of satellite images are the major challenges for finding information of the glacial lake's region. Therefore, to overcome these challenges, an automatic technique for extraction of glacial lakes using satellite imagery which is termed as GLeSI, is proposed in this article. In the GLeSI system, normalized cut (Ncut) segmentation technique with region adjacency graph and simple linear iterative clustering is proposed for accurate extraction of the glacial lake's region. The proposed system achieved an overall accuracy of 93.21%, 91.57%, 97.65% on Landsat 8 datasets for Imja, Chandra basin, and Bhaga basin glacier lake region, respectively. The qualitative and quantitative performance analysis shows the significant improvement of the proposed technique compared to state‐of‐the‐art techniques for extraction of glacial lake's region.

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Section: Methodology: the Proposed Glesi Systemmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

\times

180 km

{}^2

is identified with the help of coordinate reference systems like universal transverse mercator (UTM) and world geodetic system (WGS) 3,9 . This satellite provides eleven bands of multispectral data

{I}_1,{I}_2,\dots, {I}_{11}

.…”

Section: Introductionmentioning

confidence: 99%

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GLeSI: A system for extraction of glacial lakes using satellite imagery

Thati

Ari

2022

Concurrency and Computation

Self Cite

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“…To generate the super pixels clustering techniques were used [32]. Pseudo training samples were randomly selected from two changed and unchanged regions in kernel-based clustering techniques [33]. The clustering algorithms are used to find the final CM from the features obtained from the feature extraction [34].…”

Section: Clustering-based CD Techniquesmentioning

confidence: 99%

Change detection using multispectral satellite images: a systematic review of literature

Vasantrao,

Gupta,

Mishra

et al. 2024

Bulletin EEI

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Change detection (CD) provides information about the changes on earth’s surface over a period of time. Many algorithms have been proposed over the years for effective CD of satellite imagery. This paper presents the steps to preprocess the captured satellite images, which can be used to perform predictive analysis of earth’s surface by CD techniques. To design a highly effective system for CD, it is recommended that algorithm designers select efficient algorithms from any given application. Thus, this paper presents and investigates the review of most appropriate literature on CD, where CD techniques have been presented into three groups; i) thresholding, ii) clustering, and iii) deep learning. The first two categories mainly rely on the traditional machine learning, whereas the last one exploits the state-of-the-art deep learning models. At the end, the standard methods are summarized based on advantages, limitation, and research gap.

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“…4-9, denotes the normalization constant, CT j denotes the j-th climate type, and regSR(r, CT j ) denotes the semantic relatedness between COVID data collected from region r and those collected from regions having climate type CT j ; the CC , RC , and AC are the standard deviations corresponding to confirmed case count, recovered case count, and active case count, respectively; the 0j , 1j , 2j , and 3j , 4j , 5j are parameters regulating the means of confirmed case count, recovered case count, and active case count, respectively. These can be computed by employing maximum likelihood analysis of Expectation Maximization (EM) algorithm [11].…”

Section: Fig 4 Causal Dependency Between Climate Type and Covid-19 Case Countsmentioning

confidence: 99%

Does Climate Variability Impact COVID-19 Outbreak? An Enhanced Semantics-Driven Theory-Guided Model

Das

Ghosh

2021

SN COMPUT. SCI.

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, a life-threatening infection by novel coronavirus, has broken out as a pandemic since December 2019. Eventually, with the aim of helping the World Health Organization and other health regulators to combat COVID-19, significant research effort has been exerted during last several months to analyze how the various factors, especially the climatic aspects, impact on the spread of this infection. However, due to insufficient test and lack of data transparency, these research findings, at times, are found to be inconsistent as well as conflicting. In our work, we aim to employ a semantics-driven probabilistic framework for analyzing the causal influence as well as the impact of climate variability on the COVID-19 outbreak. The idea here is to tackle the data inadequacy and uncertainty issues using probabilistic graphical analysis along with embedded technology of incorporating semantics from climatological domain. Furthermore, the theoretical guidance from epidemiological model additionally helps the framework to better capture the pandemic characteristics. More significantly, we further enhance the impact analysis framework with an auxiliary module of measuring semantic relatedness on regional basis, so as to realistically account for the existence of multiple climate types within a single spatial region. This added notion of regional semantic relatedness further helps us to attain improved probabilistic analysis for modeling the climatological impact on this disease outbreak. Experimentation with COVID-19 datasets over 15 states (or provinces) belonging to varying climate regions in India, demonstrates the effectiveness of our semantically-enhanced theory-guided data-driven approach. It is worth noting that our proposed framework and the relevant semantic analyses are generic enough for intelligent as well as explainable impact analysis in many other application domains, by introducing minimal augmentation. Keywords Semantic Bayesian analysis • Theory-guided approach • Climate variability • COVID-19This article is part of the topical collection "Computer Aided Methods to Combat COVID-19 Pandemic" guest edited by DavidClifton, Matthew Brown, Yuan-Ting Zhang and Tapabrata Chakraborty".

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Change Detection in Landsat Images Using Unsupervised Learning and RBF-Based Clustering

Cited by 19 publications

References 45 publications

GLeSI: A system for extraction of glacial lakes using satellite imagery

GLeSI: A system for extraction of glacial lakes using satellite imagery

Change detection using multispectral satellite images: a systematic review of literature

Does Climate Variability Impact COVID-19 Outbreak? An Enhanced Semantics-Driven Theory-Guided Model

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