Reconstruction of multiple image-based high-resolution digital elevation model using planetary reflectance행성 반사율을 고려한 다중 이미지 기반 고해상도 수치 표고 모델의 복원

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 356
  • Download : 1
DC FieldValueLanguage
dc.contributor.advisorChoi, Han-Lim-
dc.contributor.advisor최한림-
dc.contributor.authorMoon, SungHyun-
dc.date.accessioned2021-05-11T19:44:51Z-
dc.date.available2021-05-11T19:44:51Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=908456&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/283638-
dc.description학위논문(박사) - 한국과학기술원 : 항공우주공학과, 2020.2,[vii, 72 p. :]-
dc.description.abstractThis dissertation discusses restoration of lunar surface topography with shadows over time. The digital elevation model (DEM) is generated by a gradient of a single image and a stereo image pair from imaging sensor or laser altimeter sensor on the orbiter. These DEMs are affected by intrinsic problems such as stereo noise in the triangulation process or the low spatial resolution of the sparse measurement point. Many image-based techniques are used to enhance the resolution of DEM which can be very challenging as the calculation of each surface point direction vector from one or partially overlapped images requires many assumptions. In this paper, an approach to enhance the resolution of coarse DEM using the information of surface normal extracted from ortho-rectified planetary image dataset is proposed. The surface normal extracted from ortho-rectified planetary image dataset and coarse DEM are well-aligned and the surface normal also offers direction vectors of uniform quality in every surface point. The key to this improvement is the separation of the surface normal as a map to overcome the ambiguity of the role of surface direction information. Using the high-resolution surface normal information, the coarse DEM is optimized to show the characteristic of terrain in the surface normal map. For performance analysis, the result of the proposed method was compared with a DEM generated using Socet Set Next-Generation Automatic Terrain Extraction (NGATE) of BAE Systems. It was demonstrated that the detail of terrain is well estimated in the high-resolution surface normal image and also in the output DEM. Restoration of surface shape information from planetary images requires planetary reflectance that are affected by many parameters including incidence, emission, and phase angles at each pixel. Complicated relationship between these angles and the terrain shape leads to difficulty in this restoration process. This paper proposes a method to effectively estimate the terrain surface shape using ortho-rectified image dataset taking into account the effect of planetary reflectance. The method extracts the Lambertian reflectance information, which depends only on the incidence angle, from the planetary reflectance by posing a sparse regression problem. This allows for more accurate estimation of the surface normal information. Moreover, this work visualizes the surface normal vector as an image for more intuitive assessment of the quality of the estimation. The effectiveness of the proposed method is demonstrated in the lunar and Mars surface image dataset.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectDigital elevation model▼aBundle adjustment▼aImage ortho-rectification▼aPhotometric stereo▼aSurface normal▼aLunar-Lambertian surface▼aAlbedo▼a3D reconstruction▼aNormal and depth fusion▼aSparse Bayesian learning▼asuper-resolution▼aconvolutional neural network-
dc.subject수치 표고 모델▼a번들 어드저스트먼트▼a이미지 정사보정▼a포토메트릭 스테레오▼a표면 법선 정보▼a달-랑베르 표면▼a알베도▼a삼차원 복원▼a표면 법선 정보 및 깊이 정보의 융합▼a희소 베이시안 학습▼a수퍼 레졸루션▼a콘볼루션 신경망-
dc.titleReconstruction of multiple image-based high-resolution digital elevation model using planetary reflectance-
dc.title.alternative행성 반사율을 고려한 다중 이미지 기반 고해상도 수치 표고 모델의 복원-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :항공우주공학과,-
dc.contributor.alternativeauthor문성현-
Appears in Collection
AE-Theses_Ph.D.(박사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0