LANDSAT Enhanced Thematic Mapper Plus Image Registration using SIFT

被引:0
|
作者
Paul, Sourabh [1 ]
Durgam, Ujwal Kumar [1 ]
Pati, Umesh C. [1 ]
机构
[1] Natl Inst Technol, Dept Elect & Commun Engn, Rourkela, India
关键词
Automatic image registration; Scale invariant feature transform; Affine transformation; Random sample consensus; MODEL;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Enhanced Thematic Mapper is a eight-band multispectral sensor in LANDSAT satellite capable of providing high resolution images. These remote sensing images have a wide range of applications in agriculture, deforestation, land cover, volcanic flow activity monitoring etc. All these applications require very accurate image registration. But, automatic registration of these images is still a vital challenge as the images have significant translation, rotation, illumination and scaling differences. Scale invariant feature transform (SIFT) is capable of extracting invariant features from image pairs and it has its own descriptor to reliably match the corresponding features. In this paper, an automatic feature based image registration is proposed using scale invariant feature transform with a reliable matching technique to increase the number of correct matched features between image pairs. At first, SIFT based feature matching is implemented with cross matching process to remove the most of the outliers. Then, fine matched features are obtained by using RANSAC algorithm. Finally, simulation results represent the effectiveness of the proposed method.
引用
收藏
页码:40 / 44
页数:5
相关论文
共 50 条
  • [41] Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon Region
    Shimabukuro, YE
    Batista, GT
    Mello, EMK
    Moreira, JC
    Duarte, V
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1998, 19 (03) : 535 - 541
  • [42] Forest fire hazard rating assessment in peat swamp forest using Landsat thematic mapper image
    Razali, Sheriza M.
    Nuruddin, A. Ainuddin
    Malek, Ismail A.
    Patah, Norizan A.
    [J]. JOURNAL OF APPLIED REMOTE SENSING, 2010, 4
  • [43] Image segmentation and classification of landsat thematic Mapper data using a sampling approach for forest cover assessment
    Bureau of Climate Change, Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, 305-8687, Japan
    不详
    [J]. Can. J. For. Res., 1 (35-43):
  • [44] Image segmentation and classification of Landsat Thematic Mapper data using a sampling approach for forest cover assessment
    Hirata, Yasumasa
    Takahashi, Tomoaki
    [J]. CANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE, 2011, 41 (01): : 35 - 43
  • [45] SPECTRAL CHARACTERIZATION OF THE LANDSAT THEMATIC MAPPER SENSORS
    MARKHAM, BL
    BARKER, JL
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1985, 6 (05) : 697 - 716
  • [46] BATHYMETRY RETRIEVAL FROM LANDSAT THEMATIC MAPPER
    SRIDHAR, PN
    MISHRA, AK
    PRASAD, KS
    [J]. INDIAN JOURNAL OF MARINE SCIENCES, 1989, 18 (02): : 147 - 148
  • [47] Classification and mapping of rangeland vegetation physiognomic composition using Landsat Enhanced Thematic Mapper and IKONOS imagery
    Mfitumukiza, David
    Kayendeke, Ellen
    Majaliwa, Mwanjalolo J. G.
    [J]. SOUTH AFRICAN JOURNAL OF GEOMATICS, 2014, 3 (03): : 259 - 271
  • [48] Evapotranspiration estimation using the Landsat-5 Thematic Mapper image over the Gyungan watershed in Korea
    Choi, Minha
    Kim, Tae Woong
    Park, Minkyu
    Kim, Seong Joon
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2011, 32 (15) : 4327 - 4341
  • [49] THE EFFECTIVE RESOLUTION ELEMENT OF LANDSAT THEMATIC MAPPER
    WILSON, AK
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1988, 9 (08) : 1303 - 1314
  • [50] RUNOFF ESTIMATION USING LANDSAT THEMATIC MAPPER DATA AND THE SCS MODEL
    SHARMA, KD
    SINGH, S
    [J]. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 1992, 37 (01): : 39 - 52