A review of remote sensing data change detection algorithms: Comparison of Faisalabad and Multan Districts, Punjab Province, Pakistan

2012 ◽  
Vol 5 (9) ◽  
Author(s):  
Farooq Ahmad
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Punjab Province ◽  
Change Detection Algorithms

scholarly journals An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Youkyung Han ◽  
Anjin Chang ◽  
Seokkeun Choi ◽  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

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.

Sign in / Sign up

Export Citation Format

Share Document