A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images

By Lorenzo Bruzzone, Francesca Bovolo. Proceedings of the IEEE, 2013

319 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/f71727a9d405c38173e687c74f9cf7f6c65c0450

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Citations in the corpus

SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images (2020, methodology): “Among the possible techniques for extracting semantic information, change detection (CD) methods are crucial for an accurate understanding of land use/cover change over a certain period at a large scale [1].”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “463 CVA and the vast majority of comparison methodologies are not constrained to image pixels with a change 464 analysis executable on pixels, primitive image-objects or meaningful image-objects (Bruzzone & Bovolo, 2013).”
Self-Supervised Change Detection in Multiview Remote Sensing Images (2021, background): “sentation ability in presence of high-resolution remote sensing images [19].”
Fast and Robust Matching for Multimodal Remote Sensing Image Registration (2019, background): “Since these multimodal data can provide complementary information for earth observation, they have been widely used in many applications, such as land-cover and land-use analysis [1], change detection [2], image fusion [3], and damage monitoring [4].”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “[160] L.”
Multitask learning for large-scale semantic change detection (2018, methodology): “As was described by Hussain et al. (2013) and Bruzzone and Bovolo (2013), VHR change detection involves several extra challenges.”
DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images (2020, background): “In high-resolution remote sensing images, ground objects have more space and shape features; hence, high-resolution remote sensing images become an important data source for change detection [10].”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “Buildings, for example, have complex appearances due to various roof superstructures, such as pipelines, chimneys, and water tanks; as a result, spectral characteristics in VHR images are signiﬁcantly heterogeneous [89,90].”