PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data

By Jinsong Deng, Ke Wang, Yiming Deng, G. Qi. 2008

656 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/664a277e184f294dbba10f29dd14b5a1b20efd36

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Citations in the corpus

DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images (2020, methodology): “Principal component analysis [14], which is a classical mathematical transformation method, obtains the difference image by extracting the effective information in the image bands, performs the difference calculation with the first principal component”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “For instance, Deng et al. (2008) applied PCA to a 316 multi-temporal data stack of Landsat and SPOT 5 imagery in order to identify changed areas for a subsequent 317 supervised change classification.”
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach (2012, methodology): “Early multi-date techniques included stacked principal component analysis (Deng et al., 2008; Li & Yeh, 1998), change vector analysis (Chen et al., 2003), or stacked multi-date composite classification (Schneider & Woodcock, 2008).”
SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images (2020, methodology): “To extract robust features, image transformation methods, such as principal component analysis (PCA) [9] and multivariate alteration detection (MAD) [10], were utilized.”
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, background): “However, the sensor characteristics and image data-related factors, such as spatial and temporal resolution, processing software and hardware, etc. determine the accuracy of LULC classiﬁcation [59].”
GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection (2019, methodology): “ormation technique, Principal Component Analysis (PCA) [15] decorrelates images [16]. However, PCA heavily relies on the statistical property of images and is especially vulnerable to unbalanced data [17]. Based on canonical correlation analysis, multivariate alteration detection (MAD) [18] is an un”
CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS (2018, methodology): “Principal component analysis (PCA) is applied for change detection in two main ways: applying PCA to images separately and then compare them using differencing or rationing (Richards, 1984) or merging the compared images into one set and then applying the PCA transform (Deng et al, 2008).”
Slow Feature Analysis for Change Detection in Multispectral Imagery (2014, methodology): “The comparative methods were CVA, difference PCA, MAD, and iteratively reweighted MAD (IRMAD) [56].”
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery (2018, methodology): “PCA is one of the best known subspace learning algorithms and can be used on both difference images and stacked images [10, 16].”
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, background): “In practice, sensor-and image-data-related factors, such as spatio-temporal and spectral resolution, availability, quality, analysis costs, and availability of processing hardware and software, determine the choice of LULC mapping data sets (Deng et al. 2008).”
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images (2018, methodology): “2) Image transformation algorithms extract the effective features of multi-temporal remote sensing images by transforming and combining their feature bands and mainly include principal component analysis (PCA) [13], multivariate alteration detection (MAD) [14], [15], Gramm–Schmidt (GS) transformatio”
Self-Supervised Change Detection in Multiview Remote Sensing Images (2021, methodology): “other popular method is the combination of PCA and Kmeans (PCA-KM) [20], which transforms and compares the bitemporal images in the feature space, and then determine the binary change map using k-means.”
A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images (2012, methodology): “This is, for instance, done in PCA-based CD approaches where only the first two (or few) principal components are selected for the solution of the BD problem [24].”
End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++ (2019, background): “Numerous PBCD approaches have been proposed to exploit the spectral and textual features of pixels, such as image algebra-based methods [7], image transformation-based methods [8,9], image classification-based methods [10], and machine learning-based methods [11–13].”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, methodology): “Deng et al. (2008) used SPOT-5 (XS) and Landsat-7 (ETM+) data and applied PCA based CD.”
Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection (2016, methodology): “…other methods: (1) unsupervised Change Vector Analysis (CVA) [8], which is an effective method for multi-spectral change detection tasks; (2) PCA [34], which is simple in computation and can be applied to real-time applications; (3) Iteratively-Reweighted Multivariate Alteration Detection…”
Object-based change detection (2012, background): “Additionally, it should be noted that several other features, such as the vegetation index of NDVI and the bands of principal component analysis (PCA), can also facilitate the improvement of change detection performance (Ward et al .”