The comparative study of three methods of remote sensing image change detection

Xu, Lian-Zeng-Ji; Zhang, Shaoqing; He, Zhongshi; Guo, Yingchun

doi:10.1109/geoinformatics.2009.5293490

Cited by 22 publications

(11 citation statements)

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“…Both groups are widely used in CD, as well as research and relevant studies (S. Liu, Bruzzone, Bovolo, & Du, 2015;C. Wu et al, 2013;Xu, Zhang, He, & Guo, 2009). We propose a new method by combining the SB-DB metrics to detect changes in multi-temporal hyperspectral imagery.…”

Section: Predictor Phasementioning

confidence: 99%

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A new land-cover match-based change detection for hyperspectral imagery

Seydi

Hasanlou

2017

European Journal of Remote Sensing

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The presence of phenomena such as earthquakes, floods and artificial human activities causes changes on the Earth's surface. Change detection (CD) is an essential tool for the monitoring and managing of resources on local and global scales. Hyperspectral imagery can provide more detailed results for detecting changes in land-cover types. The main objective of this paper is to present a new, supervised CD method by combining similarity-based and distance-based methods to increase the efficiency of already existing CD approaches. The proposed method applies in two phases and uses three different algorithms, including image differencing, modified Z-score analysis and spectral angle mapper. The efficiency of the presented method is evaluated using Hyperion multi-temporal hyperspectral imagery. The receiver-operating characteristic curve index is used for assessing and comparing the results. The results clearly demonstrate the superiority of the proposed method for the detection and production of more accurate change maps. Furthermore, the proposed method is also able to detect changes with an accuracy of more than 96%, a false alarm rate lower than 0.03 and an area under the curve of about 0.986 in overall comparison to other conventional CD techniques. In addition, this method achieved an optimal threshold value with more rapid convergence.

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Section: Predictor Phasementioning

confidence: 99%

“…ID is one of the most common methods in CD and is widely used in land-cover CD (Equation (1)). The ID algorithm is applied to the geo-referenced images in order to produce the change map (Xu et al, 2009).…”

Section: Image Differencingmentioning

confidence: 99%

A new land-cover match-based change detection for hyperspectral imagery

Seydi

Hasanlou

2017

European Journal of Remote Sensing

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“…From the rectified images, difference image (DI) is generated in the second step, usually by subtraction or ratioing [1,22,24,25]. While the former calculates the difference in intensity values of pixels between the images at two dates, the latter computes the ratio of corresponding pixels [24,26].…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

Paul

Shankar

Bhattacharyya

2020

IJGI

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Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

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“…Moreover, if unsupervised clustering methods are considered, suitable reference data are needed to assign physical meaning to the automatically found data clusters. 2,19 Further strategies for classification are given by machine learning methods (e.g., support vector machines). Such approaches may hold the advantage of being independent from the statistical properties of the input data, but they are limited by the fact of requiring a more or less detailed training database, which directly affects the quality of the analysis result.…”

Section: Introductionmentioning

confidence: 99%

Practical approach for synthetic aperture radar change analysis in urban environments

Boldt

Thiele

Schulz

et al. 2019

J. Appl. Rem. Sens.

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Change detection using remote sensing imagery is a broad and highly active field of research that has produced many different technical approaches for multiple applications. The majority of these approaches have in common that they do not deliver any detailed information concerning the type, category, or class of the detected changes. With respect to the extraction of such information, recent research often suggests that a land use classification is required. This classification can be accomplished in an unsupervised or supervised way, whereas the practicability of both strategies is more or less limited by the usage of reference or training data. Moreover, expert knowledge is needed to arrive at meaningful land use classes. An approach is presented that overcomes these drawbacks. A time series of synthetic aperture radar amplitude images is considered, enabling the detection of so-called high activity objects in urban environments. Such objects represent the basis of the investigations and denote the input for unsupervised categorization and classification procedures. The method supports even the unexperienced user in learning the actual information content leading to the capability to define a suitable scheme for change classification. Tests carried out on two different datasets suggest that the method is both practical and robust. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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The comparative study of three methods of remote sensing image change detection

Cited by 22 publications

References 1 publication

A new land-cover match-based change detection for hyperspectral imagery

A new land-cover match-based change detection for hyperspectral imagery

Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

Practical approach for synthetic aperture radar change analysis in urban environments

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