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Try some raster combination

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This commit is contained in:
Florent Guiotte 2018-08-31 15:55:52 +02:00
parent 992f912708
commit d7f1163c06
2 changed files with 250 additions and 2 deletions
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6
Notebooks/Raster Factory.ipynb
View File

@ -43,7 +43,7 @@
"C1 = ra.bulk_load('../Data/lidar/C1', 'C1', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)\n",
"C2 = ra.bulk_load('../Data/lidar/C2', 'C2', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)\n",
"C3 = ra.bulk_load('../Data/lidar/C3', 'C3', filter_treshold=.2, clip_treshold=.1, dtype=np.float32)\n",
"C12 = ra.bulk_load('../Data/lidar/C1', 'C1', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)\n",
"C12 = ra.bulk_load(['../Data/lidar/C1', '../Data/lidar/C2'], 'C12', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)\n",
"C123 = ra.bulk_load('../Data/lidar', 'C123', filter_treshold=.08, clip_treshold=.1, dtype=np.float32)"
]
},
@ -230,6 +230,8 @@
" ra.rasterize_cache(field, C2, res, method, reverse, cache)\n",
" if data_var == 'C3':\n",
" ra.rasterize_cache(field, C3, res, method, reverse, cache)\n",
" if data_var == 'C12':\n",
" ra.rasterize_cache(field, C12, res, method, reverse, cache)\n",
" if data_var == 'C123':\n",
" ra.rasterize_cache(field, C123, res, method, reverse, cache)\n",
" \n",
@ -241,7 +243,7 @@
" for reverse in (False, True):\n",
" for inter in ('linear', 'nearest', 'cubic-clip'):\n",
" for field in ('z', 'intensity', 'num_returns'):\n",
" for data in ('C1', 'C2', 'C3', 'C123'):\n",
" for data in ('C1', 'C2', 'C3', 'C12', 'C123'):\n",
" job_args.append([data, field, res, inter, reverse, '../Res/enrichment_rasters'])\n",
" \n",
"print(\"Job list length: {}\".format(len(job_args)))\n",

246
Notebooks/Raster combination.ipynb Normal file
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@ -0,0 +1,246 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"sys.path.append(\"..\")\n",
"import rasterizer\n",
"import raster_assistant as ra\n",
"\n",
"sys.path.append('../triskele/python/')\n",
"import triskele\n",
"\n",
"figsize = np.array((16, 3)) * 1.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Raster Combination\n",
"\n",
"## DSM - DTM "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"C12 = ra.bulk_load(['../Data/lidar/C1', '../Data/lidar/C2'], 'C12', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dsm = ra.rasterize_cache('z', C12, .5, 'nearest', False, '../Res/enrichment_rasters/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dtm = ra.rasterize_cache('z', C12, .5, 'nearest', True, '../Res/enrichment_rasters/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=figsize)\n",
"plt.imshow(dsm)\n",
"plt.colorbar()\n",
"plt.title('DSM')\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=figsize)\n",
"plt.imshow(dtm)\n",
"plt.colorbar()\n",
"plt.title('DTM')\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=figsize)\n",
"plt.imshow(dsm - dtm)\n",
"plt.colorbar()\n",
"plt.title('DSM - DTM')\n",
"plt.show()\n",
"\n",
"plt.imsave('../Res/dsm-dtm.png', dsm - dtm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NDVI\n",
"\n",
"With \n",
"\n",
"$NDVI = \\frac{NIR - Red}{NIR + Red}$\n",
"\n",
"and the wavelenth of the Titan\n",
"\n",
"| Lazer | Wavelenght | Color |\n",
"| --- | ---: | --- |\n",
"| C1 | 1550 nm | IR? |\n",
"| C2 | 1064 nm | NIR |\n",
"| C3 | 532 nm | Green |\n",
"\n",
"we can compute a NDVI like intensity raster with\n",
"\n",
"$NDVI_{like} = \\frac{C1 - C2}{C1 + C2}$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"C1 = ra.bulk_load('../Data/lidar/C1', 'C1', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)\n",
"C2 = ra.bulk_load('../Data/lidar/C2', 'C2', filter_treshold=.01, clip_treshold=.1, dtype=np.float32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"C1_raster = ra.rasterize_cache('intensity', C1, .5, 'linear', False, '../Res/enrichment_rasters/')\n",
"C2_raster = ra.rasterize_cache('intensity', C2, .5, 'linear', False, '../Res/enrichment_rasters/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndvi = (C1_raster - C2_raster) / (C1_raster + C2_raster)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=figsize)\n",
"plt.imshow(C1_raster)\n",
"plt.title('C1')\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=figsize)\n",
"plt.imshow(C2_raster)\n",
"plt.title('C2')\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=figsize)\n",
"plt.imshow(ndvi)\n",
"plt.title('NDVI')\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.imsave('../Res/ndvi_linear.png', ndvi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Normalized NDVI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"C1_raster /= C1_raster.max()\n",
"C2_raster /= C2_raster.max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndvi = (C1_raster - C2_raster) / (C1_raster + C2_raster)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=figsize)\n",
"plt.imshow(C1_raster)\n",
"plt.title('C1')\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=figsize)\n",
"plt.imshow(C2_raster)\n",
"plt.title('C2')\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.figure(figsize=figsize)\n",
"plt.imshow(ndvi)\n",
"plt.title('NDVI')\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n",
"plt.imsave('../Res/ndvi_normalized_linear.png', ndvi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}