From 6ae5e29d674719b4724fda96580b61af43a890f0 Mon Sep 17 00:00:00 2001 From: Yonatan Tarazona Coronel Date: Sun, 25 Jun 2023 14:24:18 -0500 Subject: [PATCH] tutorials -> new version --- docs/tutorials.md | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/docs/tutorials.md b/docs/tutorials.md index 555ac3d..0fc502b 100644 --- a/docs/tutorials.md +++ b/docs/tutorials.md @@ -34,7 +34,7 @@ rf_class = inst.RF(training_split = 0.7) Classification results: -![Original image and classified image in the left and right panel respectively.](scikit_eo_00.png){ width=100% } +![Original image and classified image in the left and right panel respectively.](images/scikit_eo_00.png){ width=100% } ## Example 02: Calibration methods for supervised classification @@ -70,7 +70,7 @@ error_mccv = inst.MCCV(split_data = data, models = ('svm', 'dt', 'rf', 'nb'), Calibration results: -![Result of the calibration methods using svm, dt, rf and nb.](scikit_eo_01.png){ width=90% } +![Result of the calibration methods using svm, dt, rf and nb.](images/scikit_eo_01.png){ width=90% } ## Example 03: Imagery Fusion. @@ -128,7 +128,7 @@ axes2.grid(False) plt.show() ``` -![Proportion of Variance and accumulative.](scikit_eo_02.png){ width=70% } +![Proportion of Variance and accumulative.](images/scikit_eo_02.png){ width=70% } ``` @@ -136,7 +136,7 @@ plt.show() fusion.get('Contributions_in_%') ``` -![Contributions of each variable in %.](scikit_eo_03.png){ width=90% } +![Contributions of each variable in %.](images/scikit_eo_03.png){ width=90% } ``` @@ -149,7 +149,7 @@ plotRGB(arr, bands = [1,2,3], title = 'Fusion of optical and radar images') plt.show() ``` -![Fusion of optical and radar images. Principal Component 1 corresponds to red channel, Principal Component 2 corresponds to green channel and Principal Component 3 corresponds to blue channel.](scikit_eo_04.png){ width=50% } +![Fusion of optical and radar images. Principal Component 1 corresponds to red channel, Principal Component 2 corresponds to green channel and Principal Component 3 corresponds to blue channel.](images/scikit_eo_04.png){ width=50% } ## Example 04: Accuracy assessment @@ -179,4 +179,8 @@ confintervalML(matrix = values, image_pred = img, pixel_size = 30, conf = 1.96, Results: -![Estimating area and uncertainty with 95%.](scikit_eo_05.png){ width=80%} +![Estimating area and uncertainty with 95%.](images/scikit_eo_05.png){ width=80%} + +```python + +```