adding files

Author: syamkakarla98

README.md

@@ -1,4 +1,4 @@
-## Dimensionality reduction and classification on Hyperspectral Images Using Python
+# Dimensionality reduction and classification on Hyperspectral Images Using Python
 
 ### Prerequisites
 
@@ -35,4 +35,45 @@ The things that you must have a decent knowledge on:
 ```
 ### Results :
 
-* The resultof the [indian_pines_pca.py]() is shown below:
+   * Here we are performing the the **dimensionality reduction**  on the **hyperspectral image** called [Indian Pines](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)     
+
+1. The result of the [indian_pines_pca.py](
+https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/indian_pines_after_pca.csv) is shown below:
+
+     * It initial result is a bargraph for the first **10 Pricipal Components according** to their _variance ratio's_ :
+
+      ![indian_pines_varianve_ratio](https://user-images.githubusercontent.com/36328597/41495831-56fff622-714e-11e8-87ab-731c11d14bab.JPG)
+      
+   Since, the initial two principal COmponents have high variance. so, we will select the initial two PC'S.
+      
+      * It second result is a scatter plot for the first **10 Pricipal Components** is :
+
+      ![indian_pines_after_pca_with_2pc](https://user-images.githubusercontent.com/36328597/41495958-603d0baa-7151-11e8-9c7c-c7452b2fb6a8.JPG)
+
+
+   * The above program resullts a dimensionally reduced [csvfile](
+https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/indian_pines_after_pca.csv) .
+ 
+2. The result of the [indian_pines_knnc.py](https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/Indian_pines_knnc.py) is given below:
+
+      * The above program will classify the Indian Pines dataset before **Principal Component Analysis(PCA)**. The classifier here used for classification is [K-Nearest Neighbour Classifier (KNNC)](http://scikitlearn.org/stable/auto_examples/neighbors/plot_classification.html)
+      * The time taken for classification is:
+      
+   ![indian_pines_classification_before_pca](https://user-images.githubusercontent.com/36328597/41496231-d2ddac0e-7157-11e8-9c14-29e89685569c.JPG)
+
+      * Then the classification accuracy of indian pines dataset before **PCA** is:
+      
+   ![indian_pines_accuracy_before_pca](https://user-images.githubusercontent.com/36328597/41495844-97a3e31e-714e-11e8-8d63-4d786317b239.JPG)    
+   
+3. The result of the [indian_pines_knnc_after_pca.py](
+https://github.com/syamkakarla98/Dimensionality-reduction-and-classification-on-Hyperspectral-Images-Using-Python/blob/master/Indian_pines_knnc_after_pca.py)
+
+   * Then the resultant classification accuracy of indian pines dataset after **PCA** is:
+      
+      ![indian_pines_accuracy_after_pca](https://user-images.githubusercontent.com/36328597/41495843-9753df04-714e-11e8-9540-0968bdb27a7f.JPG)
+
+### Conclusion :
+
+   * By performing **PCA** on the corrected indian pines dataset results **100 Principal Components(PC'S)**.
+   * since, the initial two Principal Components(PC'S) has **92.01839071674918** variance ratio. we selected two only.
+   * Initially the dataset contains the dimensions **21025 X 200** is drastically reduced to **21025 X 2** dimensions.