Change detection algorithm for multi-temporal  satellite images: a review

Bhavani, M.; Sangeetha, V.; Kalaivani, K.; K, Ulagapriya; Saritha, A

doi:10.14419/ijet.v7i2.21.12173

Cited by 2 publications

(2 citation statements)

References 14 publications

(14 reference statements)

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“…The next largest amount of variation is defined by the succeeding principal component, and this is orthogonal/ independent to the principal component that precedes it. The main advantage of PCA is that it reduces the redundancy of data [33], [40][41][42][43][44][45]. However, the disadvantage of PCA-based change detection is that it cannot provide complete change information but requires the threshold of the image to identify the changes that occurred in the area.…”

Section: ) Principal Component Analysis (Pca)mentioning

confidence: 99%

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

et al. 2022

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Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial changes due to natural phenomena and/or human-induced activities. Mango is a major fruit crop in India, but detection of changes in mango crops remains a challenging task because the reason that many perennial crops have similar reflectance profiles. Therefore, a potent change detection technique is required for different applications such as the rate of deforestation, urban developments, damage evaluation, and resource monitoring. Compared to annual and seasonal crops, relatively few studies have been conducted on change detection in perennial fruit crops. In this study, a novel change detection technique (i.e., LR-PCA) is developed using the fusion of log-ratio (LR) and principal component analysis (PCA) images derived from bi-temporal soil adjusted vegetation index (SAVI) images to extract meaningful information and detect temporal changes in the mango fruit crop areas with high change detection accuracy. The proposed approach comprised two steps: (1) SAVI images from 2015 and 2019 were used to retrieve the log-ratio (LR) and principal component (PC) images, respectively, and both the images were fused by applying the pixel-by-pixel fusion approach. (2) Fused images were classified into three classes: "positive change", "no change", and "negative change" using a derived threshold value. The results show that the LR-PCA method of change detection yields a high change detection accuracy of 92% in comparison with the other change detection methods viz. vegetation image differencing, image ratioing, PCA, and log-ratio. To validate the adaptability of the proposed algorithm, experiments with two sets of bi-temporal SAVI indices images belonging to the Sitapur district of Uttar Pradesh State determine that the proposed change detection method performs well as compared to the existing individual methods for detection of changes in mango fruit crop. The proposed method is expected to be useful for detecting changes in the area of perennial crops. In the future, an accurate and efficient change detection analysis may be helpful for developing a real-time mango fruit crop monitoring system at the national level.INDEX TERMS Landsat 8 OLI, change detection, log ratio, principal component analysis, mango crop.

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Section: ) Principal Component Analysis (Pca)mentioning

confidence: 99%

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

et al. 2022

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“…Many publications show a comprehensive review of the change detection techniques [ 45 , 46 , 47 , 48 ]. These techniques are fusion methods in the time domain and can be classified according to the fusion level, whether feature-level or decision-level fusion [ 49 ]. Feature-level fusion methods are unsupervised automatic detection methods avoiding the need to perform in situ measurements and based on change indicators chosen to enhance multi-temporal information.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing

Fabre

Gimenez

Elger

et al. 2020

Sensors

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Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle…) and plant species identity are indirect and powerful indicators of residual contamination detection in soil. Multi-temporal multispectral satellite imagery, such as the Sentinel-2 time series, is an operational environment monitoring system widely used to access vegetation traits and ensure vegetation surveillance across large areas. For this purpose, methodology based on a multi-temporal fusion method at the feature level is applied to vegetation monitoring for several years from the closure and revegetation of an ore processing site. Features are defined by 26 spectral indices from the literature and seasonal and annual change detection maps are inferred. Three indices—CIred-edge (CIREDEDGE), IRECI (Inverted Red-Edge Chlorophyll Index) and PSRI (Plant Senescence Reflectance Index)—are particularly suitable for detecting changes spatially and temporally across the study area. The analysis is conducted separately for phyto-stabilized vegetation zones and natural vegetation zones. Global and specific changes are emphasized and explained by information provided by the site operator or meteorological conditions.

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Change detection algorithm for multi-temporal satellite images: a review

Cited by 2 publications

References 14 publications

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images

Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing

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