Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics

Panuju, Dyah Retno; Paull, David; Griffin, Amy L.

doi:10.3390/rs12111781

Cited by 60 publications

(46 citation statements)

References 341 publications

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“…For the case of bi-temporal images, some techniques, including image differencing, normalized difference vegetation index (NDVI), change vector analysis (CVA), spectral features variance, and image rationing has been applied for land surface change detection and landslide detection in particular (Vázquez-Jiménez et al, 2018, Ramos-Bernal et al, 2018and Solano-Correa et al, 2018. In these techniques, mapping land surface changes or deformation caused by landslide phenomena is more achievable, but selecting the optimal thresholds to classify or separate change from no-change is still a challenge (Panuju et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automatic Landslide Detection Using Bi-Temporal Sentinel 2 Imagery

Piralilou¹,

Shahabi²,

Pazúr³

2021

giforum

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Landslide inventory data sets are required for any landslide susceptibility mapping and prediction approaches. However, generating accurate landslide inventory data sets depends on applied methods and quality of input data, for example spatial resolution for satellite imagery. Therefore, the accuracy and availability of inventories vary in different studies. This study evaluated a strategy of sudden landslide identification product (SLIP) for landslide detection using Bi-Temporal Sentinel 2 Imagery and ALOS Digital Elevation Model (DEM). The resulting landslide detection map was then compared with an improved version of SLIP based on a fuzzy overlay. The resulting probability map was classified into three classes using the natural breaks method; the third class with the highest probability was extracted as the final map. The accuracy assessment stage demonstrated that using the improved version increased the accuracy by 16% compared to the SLIP method.

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Section: Introductionmentioning

confidence: 99%

Automatic Landslide Detection Using Bi-Temporal Sentinel 2 Imagery

Piralilou¹,

Shahabi²,

Pazúr³

2021

giforum

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“…Generally, the change detection in unsupervised framework consists of three steps: preprocessing, image comparison and image analysis [1,3]. In the preprocessing step, anomalies due to radiometric and geometric factors [23,24] are removed by correction algorithms and georeferencing, respectively. Noise trapped in the images is also filtered out to some extent in this stage for improving the strength of the signals.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…From the rectified images, difference image (DI) is generated in the second step, usually by subtraction or ratioing [1,22,24,25]. While the former calculates the difference in intensity values of pixels between the images at two dates, the latter computes the ratio of corresponding pixels [24,26]. In the ratio image, the changed pixels are aligned away from "1" unsymmetrically on both sides, where the value "1" corresponds to the unchanged pixel, as equal values produce 1 on ratioing [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Whereas ratioing is efficient in inhibiting the effects of slope, sun angle and illumination variations and has been employed in a wide variety of CD problems [25], but its nonlinear nature is much criticized [22,25]. The two methods, subtraction and ratioing, are simple and easy to implement, have demonstrated exclusive behaviour on different land cover types [1,24,25]; that is, ratio method is good for detecting changes in water, soil and urban scene; difference method is good for detecting vegetation and water [24,25]; hence, they can be complementary to some extent.…”

Section: Introductionmentioning

confidence: 99%

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Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

Paul

Shankar

Bhattacharyya

2020

IJGI

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Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

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Multispectral data mining: A focus on remote sensing satellite images

Lim,

Sreevalsan‐Nair,

Daya Sagar

2023

WIREs Data Min & Knowl

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This article gives a brief overview of various aspects of data mining of multispectral image data. We focus on specifically the remote sensing satellite images acquired using multispectral imaging (MSI), given the technology used across multiple knowledge domains, such as chemistry, medical imaging, remote sensing, and so on with a sufficient amount of variation. In this article, the different data mining processes are reviewed along with state‐of‐the‐art methods and applications. To study data mining, it is important to know how the data are acquired and preprocessed. Hence, those topics are briefly covered in the article. The article concludes with applications demonstrating the knowledge discovery from data mining, modern challenges, and promising future directions for MSI data mining research.This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

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Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics

Cited by 60 publications

References 341 publications

Automatic Landslide Detection Using Bi-Temporal Sentinel 2 Imagery

Automatic Landslide Detection Using Bi-Temporal Sentinel 2 Imagery

Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

Multispectral data mining: A focus on remote sensing satellite images

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