Performance of mutual information similarity measure for registration of multitemporal remote sensing images

Chen, Hua-Mei; Varshney, Pramod K.; Arora, Manoj K.

doi:10.1109/tgrs.2003.817664

Cited by 167 publications

(23 citation statements)

References 21 publications

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“…It has been shown that a slight location error can result in a great impact on the accuracy of change detection [16,22,23] and that a registration accuracy of 0.2 pixels is required to guarantee the error of change detection is less than 10% [16,24]. It was also found that the effect of positional error is influenced by the heterogeneity of the land cover.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Congalton

Pan

2015

Remote Sensing

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Abstract:Validating or accessing the accuracy of soft classification maps has rapidly developed over the past few years. This assessment employs a soft error matrix as generalized from the traditional, hard classification error matrix. However, the impact of positional error on the soft classification is uncertain and whether the well-accepted half-pixel registration accuracy is suitable for the soft classification accuracy assessment is unknown. In this paper, a simulation analysis was conducted to examine the influence of positional error on the overall accuracy (OA) and kappa in soft classification accuracy assessment under different landscape conditions (i.e., spatial characteristics and spatial resolutions). Results showed that with positional error ranging from 0 to 3 soft pixels, the OA-error varied from 0 to 44.6 percent while the kappa-error varied from 0 to 93.7 percent. Landscape conditions with smaller mean patch size (MPS) and greater fragmentation produced greater positional error impact on the accuracy measures at spatial resolutions of 1 and 2 unit distances. However, this trend did not hold for spatial resolutions of 5 and 10 unit distances. A half of a pixel was not sufficient to keep the overall accuracy error and kappa error under 10 percent. The results indicate that for soft classification accuracy assessment the requirement for registration accuracy is higher and depends greatly on the landscape characteristics. There is a great need to consider positional error for validating soft classification maps of different spatial resolutions. OPEN ACCESSRemote Sens. 2015, 7 580

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Section: Introductionmentioning

confidence: 99%

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Congalton

Pan

2015

Remote Sensing

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“…Cross-correlation has been used for image registration during decades and it is still in use in several applications, including remote sensing [38][39][40]. Recently the use of direct gray difference measurements has decayed in favor of more powerful metrics based on information theory, such as Mutual Information (MI) [39,41,42]. Translating gray level values into the more general measure of information content provides enormous flexibility.…”

Section: Background: Image Similarity Metricsmentioning

confidence: 99%

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

et al. 2020

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Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires.

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“…They devised an open-source feature selection repository, named scikit-feature that provides 40 feature selection algorithms (including unsupervised feature selection approaches). Some selections, such as Joint Mutual Information and decision tree forward, are relatively new in earth observation applications [18].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

et al. 2019

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In the recent past, the volume of spatial datasets has significantly increased. This is attributed to, among other factors, higher sensor temporal resolutions of the recently launched satellites. The increased data, combined with the computation and possible derivation of a large number of indices, may lead to high multi-collinearity and redundant features that compromise the performance of classifiers. Using dimension reduction algorithms, a subset of these features can be selected, hence increasing their predictive potential. In this regard, an investigation into the application of feature selection techniques on multi-temporal multispectral datasets such as Sentinel-2 is valuable in vegetation mapping. In this study, ten feature selection methods belonging to five groups (Similarity-based, statistical-based, Sparse learning based, Information theoretical based, and wrappers methods) were compared based on f-score and data size for mapping a landscape infested by the Parthenium weed (Parthenium hysterophorus). Overall, results showed that ReliefF (a Similarity-based approach) was the best performing feature selection method as demonstrated by the high f-score values of Parthenium weed and a small size of optimal features selected. Although svm-b (a wrapper method) yielded the highest accuracies, the size of optimal subset of selected features was quite large. Results also showed that data size affects the performance of feature selection algorithms, except for statistically-based methods such as Gini-index and F-score and svm-b. Findings in this study provide a guidance on the application of feature selection methods for accurate mapping of invasive plant species in general and Parthenium weed, in particular, using new multispectral imagery with high temporal resolution.

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Performance of mutual information similarity measure for registration of multitemporal remote sensing images

Cited by 167 publications

References 21 publications

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

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