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  • Poster Paper 1
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  • ACRS 1990


    Land Cover/Land Uses


    The study of land use survey in the tropics using Multi Season and Multi Sensor Remote Sensing data


    Acquisition of training samples and island uses classification.
    1. Acquisition of training samples


    2. Training areas were collected as polygon data with a digitizer by comparing the image displayed on CRT with the field survey data on this maps training samples were acquired from the image pixels surrounded by the training areas data.
      Table 1 optimized correlation coefficient at each patch area
        Optimized correlation coefficient
      Patch area No. Between HRV & TM Between HRV & MESSR Between HRV & MSS
      1 0.90 0.87 0.90
      2 0.76 0.79 0.53
      3 0.83 0.94 0.91
      4 0.85 0.92 0.92
      5 0.87 0.91 0.70
      6 0.93 0.94 0.90
      7 0.83 0.93 0.95
      8 0.75 0.88 0.87


      Table 2 correlation coefficient before the registration.
        HRV
        1 2 3
      T
      M
      1 0.78 0.68 -0.49
      3 0.71 0.75 -0.28
      4 -0.66 -0.34 0.91
      7 -0.12 0.17 0.56
      M
      E
      S
      S
      R
      1 0.72 0.53 -0.63
      2 0.35 0.50 -0.03
      3 -0.60 -0.28 0.86
      4 -0.63 -0.33 0.73
      M
      S
      S
      1 0.76 0.49 -0.74
      2 0.75 0.68 -0.53
      3 -0.42 -0.08 0.72
      4 -0.52 -0.23 0.73


      Table 3 the changes of correlations coefficients at the surroundings of the optimized position of patch area
        Sift to the column direction
      -0.4 -0.2 0.0 0.2 0.4
      Shift to
      the row
      direction
      -0.6 0.44 0.62 0.79 0.82 0.78
      -0.8 0.46 0.66 0.85 0.86 0.80
      -1.0 0.40 0.67 0.90 0.87 0.77
      -1.2 0.05 0.50 0.81 0.73 0.60
      -1.4 -0.48 0.19 0.64 0.57 0.45


      Table 4 Correlation coefficients after the registration
        HRV
        1 2 3
      T
      M
      1 0.81 0.71 -0.51
      3 0.75 0.79 -0.31
      4 -0.64 -0.31 0.94
      7 -0.12 -0.22 0.56
      M
      E
      S
      S
      R
      1 0.86 0.69 -0.62
      2 0.45 0.66 0.04
      3 -0.31 -0.31 0.92
      4 -0.68 -0.37 0.92
      M
      S
      S
      1 0.77 0.51 -0.71
      2 0.65 0.63 -0.41
      3 -0.50 -0.12 0.80
      4 -0.62 -0.28 0.85



      Figure 3 Acquisition of training area

    3. Land use classification


    4. the maximum likelihood method was employed for the land use classification of training data .Classified result is shown in table image data was superior to the other image data then a single image data was classified independently .How ever when two image data were simultaneously one reason is that the combination of low resolution data with high resolution data enables us to perform the accurate classification considering the surrounding information of the pixels of high resolution data another reason which may be dominant is that the data of MSS data acquisition is different from that of the other data. Table 6, 7 and 8 show the error matrix of classified results by MESSR , MSS and the combination of MESSR and MSS respectively In the classification by MESSR urban areas and water areas are accurate and on the other hand in the classified by MSS land cover of the vegetation is accurate so in their combination all of land cover class becomes more accurate consequently this accuracy increase is one of the advantages of using the multi season and multi sensor remote sensing data.
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