From 355d1678a54b1459d09653ce90fa71ce6b287d32 Mon Sep 17 00:00:00 2001 From: senthilkumar-dimitra Date: Tue, 30 Jul 2024 22:07:07 +0530 Subject: [PATCH] fix typos --- docs/get-started.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/get-started.md b/docs/get-started.md index 46ba5c2..9b0adc8 100644 --- a/docs/get-started.md +++ b/docs/get-started.md @@ -48,7 +48,7 @@ import matplotlib as mpl import pandas as pd ``` #### 03. Image and endmembers -We will upload a satellite image as a *.tif* and endmembers as a *.dbf*. It is possible extract endmembers values using the ```extract()``` function. In this case, opload only a shapefile (point feature) of samples without extracted spectral values. Please see [tutorials](https://yotarazona.github.io/scikit-eo/tutorials/) section for more details. +We will upload a satellite image as a *.tif* and endmembers as a *.dbf*. It is possible to extract endmembers values using the ```extract()``` function. In this case, upload only a shapefile (point feature) of samples without extracted spectral values. Please see [tutorials](https://yotarazona.github.io/scikit-eo/tutorials/) section for more details. ```python path_raster = r"/home/data/LC08_232066_20190727_SR.tif" @@ -154,7 +154,7 @@ data = inst.splitData() **Parameters**: -- ```split_data```: An instance obtaind with ```splitData()```. +- ```split_data```: An instance obtained with ```splitData()```. - ```models```: Support Vector Machine (svm), Decision Tree (dt), Random Forest (rf) and Naive Bayes (nb). - ```n_iter```: Number of iterations. @@ -186,9 +186,9 @@ In ```scikit-eo``` we developed the ```fusionrs()``` function which provides us - *Contributions_in_%*: The contributions of each optical and radar band in the fusion. -#### 01. Loagind dataset +#### 01. Loading dataset -Loading a radar and optical imagery with a total of 9 bands. Optical imagery has 6 bands Blue, Green, Red, NIR, SWIR1 and SWIR2, while radar imagery has 3 bandas VV, VH and VV/VH. +Loading a radar and optical imagery with a total of 9 bands. Optical imagery has 6 bands Blue, Green, Red, NIR, SWIR1 and SWIR2, while radar imagery has 3 bands VV, VH and VV/VH. ```python path_optical = "/home/data/ex_03/LC08_003069_20180906.tif" @@ -257,7 +257,7 @@ fusion.get('Contributions_in_%') ![Contributions of each variable in %.](images/scikit_eo_03.png){ width=90% } -Here, *var1*, *var2*, ... *var12* refer to *Blue*, *Green*, ... *VV/VH* bands respectively. It can be observed that *var2* (Green) has a higher contribution percentage 16.9% than other variables. With respect to radar polarizaciones, we can note that *var8* (VH polarization) has a higher contribution 11.8% than other radar bands. +Here, *var1*, *var2*, ... *var12* refer to *Blue*, *Green*, ... *VV/VH* bands respectively. It can be observed that *var2* (Green) has a higher contribution percentage 16.9% than other variables. With respect to radar polarizations, we can note that *var8* (VH polarization) has a higher contribution 11.8% than other radar bands. #### 08. Preparing the image: