How to Build a Convolutional Neural Network (CNN) for Multi-Class Satellite Image Classification
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Conditions for Building an Effective Convolutional Neural Network (CNN) for Multi-Class Satellite Image Classification
Description: This code implements a Convolutional Neural Network (CNN) to classify satellite images into four categories: cloudy, water, green, and desert. It preprocesses the dataset by resizing, normalizing, and splitting the images, then trains the model using Conv2D layers for feature extraction and dense layers for classification. The model's performance is evaluated using metrics like accuracy, F1-score, and a confusion matrix.
Step-by-Step Process
Load Libraries: Load necessary libraries for image processing, data handling, and building deep learning models.
Retrieve and Visualize Data: Retrieve satellite images from folders, categorize them based on labels, and visualize samples.
Preprocess Images: Resize, normalize, and label images for training.
Split Data: Create training and testing datasets for evaluation.
Build CNN Model: Use Conv2D layers for feature extraction and dense layers for classification.
Evaluate and Analyze: Evaluate model performance using metrics like accuracy, F1-score, and confusion matrix.
Sample Source Code
# Import Necessary Libraries
import numpy as np
import os
from PIL import Image
import random
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.models import Model
import warnings
warnings.filterwarnings("ignore")
from sklearn.metrics import (classification_report, confusion_matrix, accuracy_score,
f1_score, recall_score, precision_score)
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