Journal ArticleDOI
Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$ -Means Clustering
TLDR
A novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering and Experimental results confirm the effectiveness of the proposed approach.Abstract:
In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering. The difference image is partitioned into h times h nonoverlapping blocks. S, S les h2, orthonormal eigenvectors are extracted through PCA of h times h nonoverlapping block set to create an eigenvector space. Each pixel in the difference image is represented with an S-dimensional feature vector which is the projection of h times h difference image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using k-means clustering with k = 2 and then assigning each pixel to the one of the two clusters by using the minimum Euclidean distance between the pixel's feature vector and mean feature vector of clusters. Experimental results confirm the effectiveness of the proposed approach.read more
Citations
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Journal ArticleDOI
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
Pedram Ghamisi,Naoto Yokoya,Jun Li,Wenzhi Liao,Sicong Liu,Javier Plaza,Behnood Rasti,Antonio Plaza +7 more
TL;DR: Rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.
Journal ArticleDOI
End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
TL;DR: A novel end-to-end CD method based on an effective encoderdecoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets, which outperforms the other state-of-the-art CD methods.
Journal ArticleDOI
Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images
TL;DR: A novel supervised change detection method based on a deep siamese convolutional network for optical aerial images that is comparable, even better, with the two state-of-the-art methods in terms of F-measure.
Journal ArticleDOI
A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images
TL;DR: A deeply supervised image fusion network (IFN) is proposed for change detection in high resolution bi-temporal remote sensing images and outperforms four benchmark methods derived from the literature, by returning changed areas with complete boundaries and high internal compactness compared to the state-of-the-art methods.
Journal ArticleDOI
DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images
TL;DR: The weighted double-margin contrastive loss is proposed to address the imbalanced sample is a serious problem in change detection, i.e., unchanged samples are much more abundant than changed samples, which is one of the main reasons for pseudochanges.
References
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Book
Digital Image Processing 3rd Edition
TL;DR: Digital image processing 3rd edition free ebooks download, ece 643 digital image processing i chapter 5, gonzfm i xxii 5 1.
Journal ArticleDOI
Automatic analysis of the difference image for unsupervised change detection
Lorenzo Bruzzone,D.F. Prieto +1 more
TL;DR: The authors propose two automatic techniques (based on the Bayes theory) for the analysis of the difference image that allow an automatic selection of the decision threshold that minimizes the overall change detection error probability under the assumption that pixels in the difference picture are independent of one another.
Journal ArticleDOI
A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment
TL;DR: Experimental results that are obtained on multitemporal RADARSAT-1 SAR images of the Sumatra Island, Indonesia, confirm the effectiveness of both the proposed SBA and the presented system for tsunami-damage assessment.
Journal ArticleDOI
A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure
TL;DR: This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images by using a selective Bayesian thresholding for deriving a pseudotraining set that is necessary for initializing an adequately defined binary semisupervised support vector machine classifier.
Journal ArticleDOI
A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks
TL;DR: A context-sensitive technique for unsupervised change detection in multitemporal remote sensing images based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times is proposed.
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Automatic analysis of the difference image for unsupervised change detection
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