Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data

2014 ◽  
pp. 237-272 ◽  
Author(s):  
Konstantinos Karantzalos
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Urban Environments ◽  
Sensing Data ◽  
Recent Advances ◽  
2D And 3D

scholarly journals A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

2021 ◽  
Vol 13 (3) ◽  
pp. 440
Author(s):  
Haiming Zhang ◽  
Mingchang Wang ◽  
Fengyan Wang ◽  
Guodong Yang ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Network Architecture ◽  
Remote Sensing Data ◽  
Earth Observation ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Data Sets ◽  
Sensing Data ◽  
2D And 3D

Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.

scholarly journals Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

2008 ◽  
Vol 46 (6) ◽  
pp. 1822-1835 ◽  
Author(s):  
G. Camps-Valls ◽  
L. Gomez-Chova ◽  
J. Munoz-Mari ◽  
J.L. Rojo-Alvarez ◽  
M. Martinez-Ramon
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Data Classification ◽  
Sensing Data

scholarly journals Heterogeneous Change Detection on Remote Sensing Data with Self-Supervised Deep Canonically Correlated Autoencoders

2021 ◽  
Author(s):  
Federico Figari Tomenotti
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Learning Framework ◽  
Novel Approach ◽  
Image Translation ◽  
Recent Developments ◽  
Different Sources

Change detection is a well-known topic of remote sensing. The goal is to track and monitor the evolution of changes affecting the Earth surface over time. The recently increased availability in remote sensing data for Earth observation and in computational power has raised the interest in this field of research. In particular, the keywords “multitemporal” and “heterogeneous” play prominent roles. The former refers to the availability and the comparison of two or more satellite images of the same place on the ground, in order to find changes and track the evolution of the observed surface, maybe with different time sensitivities. The latter refers to the capability of performing change detection with images coming from different sources, corresponding to different sensors, wavelengths, polarizations, acquisition geometries, etc. This thesis addresses the challenging topic of multitemporal change detection with heterogeneous remote sensing images. It proposes a novel approach, taking inspiration from recent developments in the literature. The proposed method is based on deep learning - involving autoencoders of convolutional neural networks - and represents an exapmple of unsupervised change detection. A major novelty of the work consists in including a prior information model, used to make the method unsupervised, within a well-established algorithm such as the canonical correlation analysis, and in combining these with a deep learning framework to give rise to an image translation method able to compare heterogeneous images regardless of their highly different domains. The theoretical analysis is supported by experimental results, comparing the proposed methodology to the state of the art of this discipline. Two different datasets were used for the experiments, and the results obtained on both of them show the effectiveness of the proposed method.

scholarly journals Change Detection Based Building Damage Assessment Method Using Radar Imageries with GLCM Textural Parameters

2020 ◽  
Author(s):  
Asset Akhmadiya ◽  
Nabi Nabiyev ◽  
Khuralay Moldamurat ◽  
Kanagat Dyusekeev ◽  
Sabyrzhan Atanov
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Damage Assessment ◽  
Central Italy ◽  
Remote Sensing Data ◽  
Research Paper ◽  
Building Damage ◽  
Main Task ◽  
Radar Remote Sensing ◽  
Sensing Data

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

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