Shadow–highlight feature matching automatic small crater recognition using high-resolution digital orthophoto map from Chang’E Missions

Chang’e-5, China’s first sample return lunar mission, will be launched in 2019, and the planned landing area is near Mons Rümker in Oceanus Procellarum. High-resolution and high-precision mapping of the landing area is of great importance for supporting scientific analysis and safe landing. This paper proposes a systematic method for large area seamless digital orthophoto map (DOM) generation, and presents the mapping result of Chang’e-5 landing area using over 700 LROC NAC images. The developed method mainly consists of two stages of data processing: stage 1 includes subarea block adjustment with rational function model (RFM) and seamless subarea DOM generation; stage 2 includes whole area adjustment through registration of the subarea DOMs with thin plate spline model and seamless DOM mosaicking. The resultant seamless DOM coves a large area (20&deg; longitude &times; 4&deg; latitude) and is tied to the widely used reference DEM &ndash; SLDEM2015. As a result, the RMS errors of the tie points are all around half pixel in image space, indicating a high internal precision; the RMS errors of the control points are about one grid cell size of SLDEM2015, indicating that the resultant DOM is tied to SLDEM2015 well.

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Cratering and age of the small Saturnian satellites

Astronomy and Astrophysics ◽

10.1051/0004-6361/201834660 ◽

2019 ◽

Vol 627 ◽

pp. A12

Author(s):

N. L. Rossignoli ◽

R. P. Di Sisto ◽

M. Zanardi ◽

A. Dugaro

Keyword(s):

Dynamical System ◽

Theoretical Model ◽

High Resolution ◽

Solar System ◽

Size Distribution ◽

Frequency Distribution ◽

Size Frequency Distribution ◽

Crater Size ◽

Small Crater ◽

High Resolution Images

Context. The small (≤135 km mean radius) satellites of Saturn are closely related to its rings and together they constitute a complex dynamical system where formation and destruction mechanisms compete against each other. The Cassini-Huygens mission provided high-resolution images of the surfaces of these satellites and therefore allowed for the calculation of observational crater counts. Aims. We model the cratering process by Centaur objects on the small Saturnian satellites, and compare our results with the observational crater counts obtained from the Voyager and Cassini missions. Methods. Using a theoretical model previously developed we calculate the crater production on these satellites considering two slopes of the size-frequency distribution (SFD) for the smaller objects of the Centaur population and compare our results with the available observations. In addition, we consider the case of catastrophic collisions between these satellites and Centaur objects and calculate the age of formation of those satellites that suffer one or more disruptions. Results. In general we find that the observed crater distributions are best modeled by the crater size distribution corresponding to the s2 = 3.5 index of the SFD of impactors with diameters smaller than 60 km. However, for crater diameters D ≲ 3–8 km (which correspond to impactor diameters d ~ 0.04–0.15 km), the observed distributions become flatter and deviate from our results, which may evidence processes of erosion and/or crater saturation at small crater sizes or a possible break in the SFD of impactors at d ~ 0.04–0.15 km to a much shallower differential slope of approximately − 1.5. Our results suggest that Pan, Daphnis, Atlas, Aegaeon, Methone, Anthe, Pallene, Calypso, and Polydeuces suffered one or more catastrophic collisions over the age of the solar system, the younger being associated to arcs with ages of ~108 yr. We have also calculated surface ages for the satellites, which indicate ongoing resurfacing processes.

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Automatic Registration between High Resolution SAR Image and GIS Road Network Based on Pairwise Constraint and ICM Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.3639 ◽

2014 ◽

Vol 989-994 ◽

pp. 3639-3643

Author(s):

Hai Gang Sui ◽

Feng Hua ◽

Guang Chen ◽

Chuan Xu

Keyword(s):

High Resolution ◽

Disaster Response ◽

Feature Matching ◽

Monitoring And Evaluation ◽

Speckle Noise ◽

Line Pair ◽

Sar Image ◽

Pairwise Constraint ◽

Traditional Use ◽

Gis Data

Synthetic aperture radar (SAR) technique has been widely applied in disaster monitoring and evaluation, because of its all-weather and all-time capability. In particular, the integration of high resolution SAR image and GIS data has been proved to be an effective solution to change detection for rapid disaster response. However, accurate registration of SAR image and GIS data remains a challenging task. On the one hand, SAR image and GIS data are two different representation levels of the real world. GIS data lacks of grey value and texture information, thus traditional use of grey-based and texture-based feature descriptors for feature matching in GIS data is impossible. On the other hand, the extraction of matching primitives is difficult in the SAR image due to the speckle noise. Moreover, though features can be extracted in the SAR image, the matching can be difficult since features may be fractured and missing. In this paper, we propose a new method for vector-to-SAR image registration which utilizes conjugate line-pair intersections as matching primitives. The core idea consists in two aspects: 1) Different from traditional road intersection based registration methods, conjugate line-pair intersections are employed as matching primitive in the proposed method. 2) To find out the optimal set of matching features, a matching technique that using pairwise constraint and the iterative conditional mode (ICM) optimization algorithm is introduced. Experiment results proved the reliability and feasibility of the proposed method.

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Visual Localization of the Tianwen-1 Lander Using Orbital, Descent and Rover Images

Remote Sensing ◽

10.3390/rs13173439 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3439

Author(s):

Wenhui Wan ◽

Tianyi Yu ◽

Kaichang Di ◽

Jia Wang ◽

Zhaoqin Liu ◽

...

Keyword(s):

High Resolution ◽

Initial Result ◽

Visual Localization ◽

High Resolution Imaging ◽

Localization Accuracy ◽

Imaging Camera ◽

Resolution Imaging ◽

Exploration Mission ◽

Digital Orthophoto ◽

Mission Operations

Tianwen-1, China’s first Mars exploration mission, was successfully landed in the southern part of Utopia Planitia on 15 May 2021 (UTC+8). Timely and accurately determining the landing location is critical for the subsequent mission operations. For timely localization, the remote landmarks, selected from the panorama generated by the earliest received Navigation and Terrain Cameras (NaTeCam) images, were matched with the Digital Orthophoto Map (DOM) generated by high resolution imaging camera (HiRIC) images to obtain the initial result based on the triangulation method. Then, the initial localization result was refined by the descent images received later and the NaTeCam DOM. Finally, the lander location was determined to be (25.066°N, 109.925°E). Verified by the new orbital image with the lander and Zhurong rover visible, the localization accuracy was within a pixel of the HiRIC DOM.

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A high-speed feature matching method of high-resolution aerial images

Journal of Real-Time Image Processing ◽

10.1007/s11554-020-01012-8 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zhiyong Peng ◽

Jun Wu ◽

Yongjun Zhang ◽

Xianhua Lin

Keyword(s):

High Resolution ◽

High Speed ◽

Feature Matching ◽

Aerial Images ◽

Matching Method

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An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration

Remote Sensing ◽

10.3390/rs13091657 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1657

Author(s):

Junyan Lu ◽

Hongguang Jia ◽

Tie Li ◽

Zhuqiang Li ◽

Jingyu Ma ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Image Registration ◽

Feature Matching ◽

Local Area ◽

Remote Sensing Image ◽

Remote Sensing Images ◽

Fine Grained ◽

Feature Based ◽

Instance Segmentation

Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features for local area matching. Then, a feature-based method combined with the instance segmentation results is adopted to acquire more accurate local feature matching. Finally, multi-constraints based on the instance segmentation results are introduced to work on the outlier removal. In the experiments of high-resolution remote sensing image registration, the proposal effectively copes with the circumstance of the sensed image with poor positioning accuracy. In addition, the method achieves superior accuracy and competitive robustness compared with state-of-the-art feature-based methods, while being rather efficient.

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