Spectral_Modulation.ipynb

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    "colab": {
      "name": "Spectral_Modulation.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "source": [
        "### Spectral Modulation for Multi-Spectral Images\n",
        "This function code performs two things based on-\n",
        "- Level 2 corrected images of the original dataset.\n",
        "- Modulation based classification for multi-spectral images.\n",
        "\n",
        "Idea for the Spectral Modulation code:\n",
        "- Suppose there are 4 bands- b1,b2,b3,b4 and pattern as (120,110,100,90) then modulation pattern is:'222222' based on (120>110,120>100,120>90,110>100,110>90,100>90)\n",
        "- '2' represents preceeding band value is greater\n",
        "- '1' represents succeeding band value is greater\n",
        "- '0' represents both band values are equal\n",
        "- Each pixel vector of size 4 is identified in underlying classes. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKef0_vJUAnD",
        "outputId": "49cb5618-5ceb-44ea-bfe5-28842f7a8b03"
      },
      "source": [
        "!apt-get update\n",
        "#Step 2\n",
        "!apt-get install libgdal-dev -y\n",
        "#Step 3\n",
        "!apt-get install python-gdal -y\n",
        "#Step 4\n",
        "!apt-get install python-numpy python-scipy -y\n",
        "#Step 5"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52TuHhZAUqDm"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "import gdal\n",
        "import osr\n",
        "import os"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlmTjquYfBA0"
      },
      "source": [
        "This function reads the tif file on a given path and returns its data , projection, geotransform and targetprj.\n",
        "\n",
        "## Input:\n",
        "- band(str) : Name of the band.\n",
        "- PATH(str): path of the file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSfLL7KwU2fu"
      },
      "source": [
        "def readBand(band,PATH):\n",
        "    img = gdal.Open(PATH)\n",
        "    data = np.array(img.GetRasterBand(1).ReadAsArray())\n",
        "    spatialRef = img.GetProjection()\n",
        "    geoTransform = img.GetGeoTransform()\n",
        "    targetprj = osr.SpatialReference(wkt = img.GetProjection())\n",
        "    \n",
        "    return data, spatialRef, geoTransform, targetprj\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "quWe-YLLhRkN"
      },
      "source": [
        "This function converts np array to a raster file and store in tif format.\n",
        "## Input:\n",
        "- array: numpy array containing DN values\n",
        "-geoTransform: affine transformation coefficients\n",
        "- projection: projection info\n",
        "- filename: output path"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSikJrkDU_NJ"
      },
      "source": [
        "def writeBand(array, geoTransform, projection, filename, classified=False, dtype=gdal.GDT_UInt16):\n",
        "  \n",
        "    pixels_x = array.shape[1]\n",
        "    pixels_y = array.shape[0]\n",
        "    \n",
        "    driver = gdal.GetDriverByName('GTiff')\n",
        "    dataset = driver.Create(\n",
        "            filename,\n",
        "            pixels_x,\n",
        "            pixels_y,\n",
        "            1,\n",
        "            dtype)\n",
        "    dataset.SetGeoTransform(geoTransform)\n",
        "    dataset.SetProjection(projection)\n",
        "    \n",
        "    band = dataset.GetRasterBand(1)\n",
        "    if classified:\n",
        "        colors = gdal.ColorTable()\n",
        "        for class_value in classified:\n",
        "            colors.SetColorEntry(class_value[0], tuple(np.random.choice(range(256), size=3)))\n",
        "        band.SetRasterColorTable(colors)\n",
        "        band.SetRasterColorInterpretation(gdal.GCI_PaletteIndex)\n",
        "        \n",
        "    band.SetNoDataValue(0)\n",
        "    band.WriteArray(array)\n",
        "    dataset.FlushCache()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeVoy1BjiNH6"
      },
      "source": [
        "This is spectral modulation function for feature band classification\n",
        "## Input:\n",
        "- bands: Tuple of bands on which spectral modulation is to be performed.\n",
        "-outpath: output path"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RB4MrJ_7VKs3"
      },
      "source": [
        "def spectral_modulation(bands,outpath):\n",
        "  rows,cols=bands[0][0].shape\n",
        "  classified_image=np.zeros((rows,cols),dtype=np.float16)\n",
        "  water_image=np.zeros((rows,cols),dtype=np.float16)\n",
        "  classes={}\n",
        "  class_count=1\n",
        "\n",
        "  for row in range(rows):\n",
        "    for col in range(cols):\n",
        "      spectra=[]\n",
        "      for band in bands:\n",
        "        spectra.append(band[0][row][col])\n",
        "      \n",
        "      modulation=\"\"\n",
        "\n",
        "      for i in range(len(spectra)):\n",
        "        for j in range(i+1,len(spectra)):\n",
        "          if spectra[i]<spectra[j]:\n",
        "            modulation+='2'\n",
        "          elif spectra[i]>spectra[j]:\n",
        "            modulation+='1'\n",
        "          else:\n",
        "            modulation+='0'\n",
        "      \n",
        "      if modulation in classes:\n",
        "        classified_image[row][col]=classes[modulation][0]\n",
        "        classes[modulation][1]+=1\n",
        "      else:\n",
        "        classified_image[row][col]=class_count\n",
        "        classes[modulation]=[class_count,0]\n",
        "        class_count+=1\n",
        "\n",
        "      if  (modulation=='111222') or (modulation=='221011') or (modulation=='111111'):\n",
        "        water_image[row][col] = 0\n",
        "      else:\n",
        "        water_image[row][col] = 255\n",
        "\n",
        "  print(classes)\n",
        "  print(water_image)\n",
        "  print(classified_image)\n",
        "  writeBand(classified_image, bands[0][2], bands[0][1], '/content/drive/MyDrive/SPEC MOD/LS8_TM_all_classes__' +outpath+ '.tif', classified=tuple(classes.values()))\n",
        "  writeBand(water_image, bands[0][2], bands[0][1], '/content/drive/MyDrive/SPEC MOD/LS8_TM_swir_water__' + outpath + '.tif')\n"
      ],
      "execution_count": 5,
      "outputs": []
    }
  ]
}