A Stable Land Cover Patches Method for Automatic Registration of Multitemporal Remote Sensing Images

Cao, Shixiu; Zhu, Xiufang; Pan, Yaozhong; Yu, Qiuyan

doi:10.1109/jstars.2013.2264312

Cited by 13 publications

(3 citation statements)

References 38 publications

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“…This registration method is usually carried out for all the 'masterto-slave' combinations, as shown in Figure 1a. Corresponding features may be measured manually, but several procedures have been developed for their automatic extraction [10][11][12]. Such features are used for estimating a geometric 2D transformation to map the images to each other and to obtain pixel-to-pixel overlap after resampling.…”

Section: Introductionmentioning

confidence: 99%

Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

2018

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This paper describes an automatic multi-image robust alignment (MIRA) procedure able to simultaneously co-register a time series of medium-resolution satellite images in a bundle block adjustment (BBA) fashion. Instead of the direct co-registration of each image with respect to a reference ‘master’ image on the basis of corresponding features, MIRA also considers those tie points that may be not be shared with the master, but they only connect the other images (‘slaves’) among them. In a first stage, tie points are automatically extracted by using pairwise feature-based matching based on the SURF operator. In a second stage, such extracted features are re-ordered to find corresponding tie points visible on multiple image pairs. A ‘master’ image is then selected with the only purpose to establish the datum of the final image alignment and to instantiate the computation of approximate registration parameters. All the available information obtained so far is fed into a least-squares BBA to estimate the unknowns, which include the registration parameters and the coordinates of tie points re-projected in the ‘master’ image space. The analysis of inner and outer reliability of the observations is applied to assess whether the residual blunders may be located using data snooping, and to evaluate the influence of undetected outliers on the final registration results. Three experiments with simulated datasets and one example consisting of eleven Landsat-5/TM images are reported and discussed. In the case of real data, results have been positively checked against the ones obtained by using alternative procedures (BBA with manual measurements and ‘slave-to-master’ registration based on automatically extracted tie points). These experiments have confirmed the correctness of the MIRA approach and have highlighted the potential of the inner control on the final quality of the solution that may come from the reliability analysis.

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Section: Introductionmentioning

confidence: 99%

Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

2018

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“…However, the raw dense LiDAR point cloud can offer higher geographic accuracy [12] as the raw data does not depend on grid size. Besides, image registration and rasterization may introduce additional inaccuracy [13,14]. It would be valuable to develop a multi-modal fusion algorithm that can handle directly the HSI imagery and raw LiDAR point cloud.…”

Section: Introductionmentioning

confidence: 99%

Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty

Du,

Zare

2018

Preprint

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In remote sensing, each sensor can provide complementary or reinforcing information.It is valuable to fuse outputs from multiple sensors to boost overall performance. Previous supervised fusion methods often require accurate labels for each pixel in the training data. However, in many remote sensing applications, pixel-level labels are difficult or infeasible to obtain. In addition, outputs from multiple sensors may have different levels of resolution or modalities (such as rasterized hyperspectral imagery versus LiDAR 3D point clouds). This paper presents a Multiple Instance Multi-Resolution Fusion (MIMRF) framework that can fuse multi-resolution and multi-modal sensor outputs while learning from ambiguously and imprecisely labeled training data. Experiments were conducted on the MUUFL Gulfport hyperspectral and LiDAR data set and a remotely-sensed soybean and weed data set. Results show improved, consistent performance on scene understanding and agricultural applications when compared to traditional fusion methods.

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“…light and atmosphere, the nonlinear radiation distortion s between images always exist [5]. In general, images with overlapping areas obtained by different sensors at different times and imaging angles are called multi-modal images, which are more prone to nonlinear radiation distortion [6].…”

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confidence: 99%

Feature Matching of Multimodal Images Based on Nonlinear Diffusion and Progressive Filtering

Xiong

Fang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Traditional image feature matching methods cannot obtain satisfactory results for multi-modal images in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences and geometric distortion. The key to multi-modal image matching is trying to eliminate the nonlinear radiation distortion and extract more robust features. This paper proposes a new robust feature matching method for multi-modal images. Our method starts by detecting feature points on phase congruency maps in nonlinear scale space and then removing mismatches by progressive filtering. Specifically, the phase congruency maps are generated by the Log-Gabor filter. Then, the feature points on phase congruency maps are detected in nonlinear scale space constructed by the nonlinear diffusion filter. Subsequently, the structure descriptor is established by the Log-Gabor filter, and the initial correspondences are constructed by bilateral matching. Finally, an iterative strategy is used to remove mismatches by progressive filtering. We perform comparison experiments on our proposed method with the SIFT, RIFT, VFC, LLT, LPM, and mTopKPR methods using multi-modal images. The algorithms of each method are comprehensively evaluated both qualitatively and quantitatively. Our experimental results indicate the superiority of our method over the other six matching methods.

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A Stable Land Cover Patches Method for Automatic Registration of Multitemporal Remote Sensing Images

Cited by 13 publications

References 38 publications

Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

Multi-Image Robust Alignment of Medium-Resolution Satellite Imagery

Multi-Resolution Multi-Modal Sensor Fusion For Remote Sensing Data With Label Uncertainty

Feature Matching of Multimodal Images Based on Nonlinear Diffusion and Progressive Filtering

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