Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic

Sheng, Yongwei; Shah, Chintan; Smith, L. C.

doi:10.1109/lgrs.2008.916646

Cited by 54 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Pekel et al [24] used all the Landsat images to detect surface water body changes in the last three decades for the entire globe. The methods of water body extraction can generally be divided into two categories; one is the traditional supervised and unsupervised classifications using a single band or multiple bands [25][26][27], and the other one is the water-related spectral index-(water index for short) and threshold-based approach [28][29][30][31][32]. Generally, supervised classification technologies based on spectral signature analysis can effectively identify and detect large water bodies, but these approaches are constrained when performing a rapid and reproducible large scale water body mapping [33].…”

Section: Introductionmentioning

confidence: 99%

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Zhou

Dong

Xiao

et al. 2017

Water

179

105

View full text Add to dashboard Cite

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index-and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

show abstract

Section: Introductionmentioning

confidence: 99%

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Zhou

Dong

Xiao

et al. 2017

Water

179

105

View full text Add to dashboard Cite

show abstract

“…In order to detect temporal changes taking place in object space, images captured at different times need to be spatially aligned (Sheng et al, 2008). Image registration in modern photogrammetry approaches (Behling et al, 2014, Zitová et al, 2003 integrates computer vision techniques for automated processing workflows and often it involves the so called Structure from Motion (SfM) (Westoby et al, 2012).…”

Section: Methods Overviewmentioning

confidence: 99%

Co-Registration of Multitemporal Uav Image Datasets for Monitoring Applications: A New Approach

Aicardi¹,

Nyapwere²,

Nex³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Commission ICWG I/VbKEY WORDS: Multi-temporal, UAV, image-based approach, image registration, photogrammetry ABSTRACT:In the last years we have witnessed a rapid development of UAVs (Unmanned Aerial Vehicles), especially for image collection. One of the advantages is the possibility to perform high resolution and repeated flights in a cheap way to detect changes over time. Thus, dynamic scenes can be monitored acquiring image blocks in different epochs in a flexible way. Anyway, most of UAVs are not able to provide accurate direct geo-referencing information, so image blocks from different epochs still need to be co-registered to efficiently detect changes. This task is mostly completed using GCPs (Ground Control Points), although this approach is time consuming as manual intervention is needed. This paper aims at investigating new techniques to automate the co-registration of image blocks without the use of GCPs, just relying on an image based co-registration (IBCR) approach. The image alignment is initially performed on a reference (anchor) epoch and the registration of the following (slave) epochs is performed including some (anchor) images from the reference epoch with fixed external orientation parameters. This allows constraining the Bundle Block Adjustment of the slave epoch to be consistent with the reference one. The study involved the use of 10 multi-temporal image block over a large building construction site, and spanning a time frame of 2 years. Different tests have been performed for the reference image choice with a manual approach and then evaluating the reached accuracy. The performed tests on the chosen test site have shown that the accuracy of the proposed methodology provides results comparable to the common GCPs registration approach.

show abstract

“…Feature-based registration algorithms extract distinctive, highly informative feature objects first. Some operators, such as scale-invariant feature transform (SIFT) [19][20][21], curvature scale space (CSS), Harris [22], speed up robust features (SURF) [23], and features from accelerated segment test (FAST) [24] are frequently used for feature point extraction. Many studies have compared the performances of various point detectors, proving that only a few are useful for the registration of remote sensing images, as a result of their characteristic of being computationally intensive [25].…”

Section: Introductionmentioning

confidence: 99%

Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

et al. 2018

View full text Add to dashboard Cite

In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method can be effective for registering optical multimodal remote sensing images that have been captured with different imaging sensors.

show abstract

Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic

Cited by 54 publications

References 18 publications

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Co-Registration of Multitemporal Uav Image Datasets for Monitoring Applications: A New Approach

Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection, Window Selection, and Histogram Specification

Contact Info

Product

Resources

About