remote sensing tools examples

ytarazona · Jan 22, 2022 · 463bf02 · 463bf02
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diff --git a/examples/notebooks/01. Machine learning.ipynb b/examples/notebooks/01. Machine learning.ipynb
@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **_<div class=\"alert alert-success\"><font color='darkred'> Tutorials: Machine Learning</font></div>_**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **_<div class=\"alert alert-success\"><font color='darkblue'> @Yonatan Tarazona Coronel</font></div>_**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.0. Neural Network (NN)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "El valor es uno de los elementos fundamentales (como por ejemplo una letra o un número) que manipula un programa. Los valores que hemos visto hasta el momento son 2 (el resultado de sumar 1 + 1) y Hola, mundo.\n",
+    "\n",
+    "Estos valores son de distintos **tipos**: 2 es un entero y Hola, mundo es una cadena, llamada así porque contiene una \"cadena\" de letras. Usted (y el intérprete) puede identificar las cadenas porque están encerradas entre comillas.\n",
+    "La sentencia print también funciona con enteros:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mla import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How to run Neural Network with Python and the EOpy package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from dbfread import DBF\n",
+    "import rasterio\n",
+    "\n",
+    "path_raster = r\"G:\\4_RS_Python\\Leccion4\\Datos\\232066\\LC08_SUBSET_232066_20190727_STACK.tif\"\n",
+    "img = rasterio.open(path_raster)\n",
+    "\n",
+    "path_endmembers = r\"G:\\4_RS_Python\\Leccion4\\shp\\signaturas2018.dbf\"\n",
+    "endmembers = DBF(path_endmembers)\n",
+    "\n",
+    "clas = mla(image = img, endm = endmembers)\n",
+    "classi = clas.NN(max_iter = 400, training_split = 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# kmeans\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plt.imshow(classi.get('Classification_Map'), cmap = 'jet')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import writeRaster as wr\n",
+    "import os\n",
+    "wr.writeRaster(arr = classi.get('Classification_Map'), \n",
+    "           image = classi.get('Image'),\n",
+    "           filename = 'img2')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.0. K-means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from dbfread import DBF\n",
+    "import rasterio\n",
+    "\n",
+    "path_raster = r\"G:\\4_RS_Python\\Leccion4\\Datos\\232066\\LC08_SUBSET_232066_20190727_STACK.tif\"\n",
+    "img = rasterio.open(path_raster)\n",
+    "\n",
+    "img.read().shape\n",
+    "clas = rkmeans(image = img, n_clusters = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# kmeans\n",
+    "plt.figure(figsize=(7,7))\n",
+    "plt.imshow(clas, cmap = 'jet')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import writeRaster as wr\n",
+    "import os\n",
+    "wr.writeRaster(arr = classi.get('Classification_Map'), \n",
+    "           image = classi.get('Image'),\n",
+    "           filename = 'img2')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/notebooks/02. Spectral Mixture Analysis.ipynb b/examples/notebooks/02. Spectral Mixture Analysis.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **_<div class=\"alert alert-success\"><font color='darkred'> Tutorials: Machine Learning</font></div>_**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **_<div class=\"alert alert-success\"><font color='darkblue'> @Yonatan Tarazona Coronel</font></div>_**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.0. Spectral Mixture Analysis (SMA)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "El valor es uno de los elementos fundamentales (como por ejemplo una letra o un número) que manipula un programa. Los valores que hemos visto hasta el momento son 2 (el resultado de sumar 1 + 1) y Hola, mundo.\n",
+    "\n",
+    "Estos valores son de distintos **tipos**: 2 es un entero y Hola, mundo es una cadena, llamada así porque contiene una \"cadena\" de letras. Usted (y el intérprete) puede identificar las cadenas porque están encerradas entre comillas.\n",
+    "La sentencia print también funciona con enteros:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mla import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How to run Neural Network with Python and the EOpy package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# La ruta de la imagen\n",
+    "path_img = \"G:/4_RS_Python/Leccion5/Datos/232066/LC08_SUBSET_232066_20190727_STACK.tif\"\n",
+    "# Luego procedemos a leerla con la funcion gdal.Open()\n",
+    "img = rasterio.open(path_img)\n",
+    "\n",
+    "# Firmas espectrales puras\n",
+    "M =np.array([[8980,8508,8704,13636,16579,11420], # suelo\n",
+    "    [8207,7545,6548,16463,9725,6673], # bosque\n",
+    "    [9276,9570,10089,6743,5220,5143], # agua\n",
+    "    ])\n",
+    "#sma = sma(image = img, endmembers = M)\n",
+    "img.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(10,10))\n",
+    "plt.imshow(sma) # magma, gray, etc.\n",
+    "plt.colorbar()\n",
+    "plt.title(\"Spectral Mixture Analysis - LC8\") # Titulo\n",
+    "plt.xlabel('Columnas X') # Nombre del eje X\n",
+    "plt.ylabel('Filas Y') # Nombre del eje Y\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import writeRaster as wr\n",
+    "import os\n",
+    "wr.writeRaster(arr = classi.get('Classification_Map'), \n",
+    "           image = classi.get('Image'),\n",
+    "           filename = 'img2')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}