The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data

By Allan Aasbjerg Nielsen. IEEE Transactions on Image Processing, 2007

747 citations recorded by the source corpus.

Subfields: Ecology, Media Technology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ec15ae9a4b26f07d0ba9e0c952daf58cae8b243e

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Citations in the corpus

Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances (2016, methodology): “In [36], Nielsen aligns domains with canonical correlation analysis (CCA) and performs change detection therein.”
Slow Feature Analysis for Change Detection in Multispectral Imagery (2014, methodology): “The IWD is available for MAD and SFA [56].”
Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network (2020, methodology): “[14] [15] proposed multivariate alteration detection (MAD) and iteratively reweighting multivariate alteration detection (IRMAD).”
GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection (2019, methodology): “In order to demonstrate the effectiveness and generality of the proposed method, we compare it with other state-of-the-art methods, including CVA [34], PCA-CVA [35], IR-MAD [19], support vector machines (SVMs) [21], and patch-based CNN, which takes preprocessed HSIs as input.”
A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion (2015, methodology): “We applied iteratively reweighted multivariate alteration detection (IRMAD) transformation (Nielsen, 2007) to select pixels with a high no-change probability (i.e., N0.”
A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (2013, methodology): “…are defined by using: i) fuzzy set theory [51], [52], [53], [54], [55], [56], [57], [58], [59]; ii) data-driven transformations (e.g., Principal Component Analysis [37]; Independent Component Analysis [60], Multivariate Alteration Detection [36], [61], [62]); and iii) similarity measures [63].”
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images (2018, methodology): “IRMAD have an AUC value of 0.9536, which is slightly better The change detection results obtained by Kmeans method are presented in Table II.”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “It was extended to an iterative reweighted version (IR-MAD) [296] to better emphasize and detect changes.”
Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, methodology): “…(CVA) [12], [13], principal component analysis (PCA) [14], [15], independent component analysis (ICA) [16]), image classiﬁcation [17], [18], and other advanced algorithms, such as slow feature analysis (SFA) [18]–[20] and iteratively reweighted multivariate alteration detection (IR-MAD) [21].”
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges (2019, methodology): “The extended version, termed iterative reweighted MAD (IR-MAD), was proposed in [59] to better model the change and no-change background representations.”
Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation (2008, methodology): “In his original formulation of the iterative procedure, Nielsen (2007) standardized the MAD variates using the standard deviations of all of the observations, rather than using the iteratively re-weighted σMi values given by Eq.”
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model (2024, methodology): “To cope with this, researchers proposed object-based CD methods [10], which take the object consisting of homogeneous pixels as the basic unit for CD.”
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images (2018, background): “Considering the importance of change detection in earth resources surveys, environmental monitoring, regional planning, and global macroresearch, it has attracted a lot of research interests [8]–[15].”
A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure (2008, methodology): “Block scheme of the proposed approach. detection [10], [11].”
A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection (2021, methodology): “The iteratively reweighted MAD (IR-MAD) method [45] applied different weights to each pixel on the basis of MAD.”
Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection (2016, methodology): “…information to detect a changed region more easily than when using the original spectral information of multi-temporal images, such as in Principal Component Analysis (PCA) [14], Multivariate Alteration Detection (MAD) [15], subspace learning [16,17], sparse learning [18] and slow features [19].”