Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

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Change detection techniques

Selecting and interpreting measures of thematic classification accuracy

One of the main problems related to unsupervised change detection methods based on the "difference image" lies in the lack of efficient automatic techniques for discriminating between changed and unchanged pixels in the difference image. Such discrimination is usually performed by using empirical strategies or manual trial-and-error procedures, which affect both the accuracy and the reliability of the change-detection process. To overcome such drawbacks, in this paper, the authors propose two automatic techniques (based on the Bayes theory) for the analysis of the difference image. One allows an automatic selection of the decision threshold that minimizes the overall change detection error probability under the assumption that pixels in the difference image are independent of one another. The other analyzes the difference image by considering the spatial-contextual information included in the neighborhood of each pixel. In particular, an approach based on Markov Random Fields (MRFs) that exploits interpixel class dependency contexts is presented. Both proposed techniques require the knowledge of the statistical distributions of the changed and unchanged pixels in the difference image. To perform an unsupervised estimation of the statistical terms that characterize these distributions, they propose an iterative method based on the Expectation-Maximization (EM) algorithm. Experimental results confirm the effectiveness of both proposed techniques.

/pdf/automatic-analysis-of-the-difference-image-for-unsupervised-2g1i5n5i4r.pdf

Automatic analysis of the difference image for unsupervised change detection

https://www.fs.fed.us/rm/pubs_other/rmrs_1998_stehman_s001.pdf

Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles

The world's forest ecosystems are in a state of permanent flux at a variety of spatial and temporal scales. Monitoring techniques based on multispectral satellite‐acquired data have demonstrated potential as a means to detect, identify, and map changes in forest cover. This paper, which reviews the methods and the results of digital change detection primarily in temperate forest ecosystems, has two major components. First, the different perspectives from which the variability in the change event has been approached are summarized, and the appropriate choice of digital imagery acquisition dates and interval length for change detection are discussed. In the second part, preprocessing routines to establish a more direct linkage between digital remote sensing data and biophysical phenomena, and the actual change detection methods themselves are reviewed and critically assessed. A case study in temperate forests (north‐central U.S.A.) then serves as an illustration of how the different change detectio...

E. Ledrew

Papers

The determination of optimal threshold levels for change detection using various accuracy indices