unet_draft.py

import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, concatenate, UpSampling2D
from tensorflow.keras.models import Model

# Load your dataset and preprocess it
def load_and_preprocess_data():
    # Load remote sensing images and their ground truth segmentation masks
    # Modify this function according to your dataset structure
    images, masks = [], []
    for img_file, mask_file in zip(image_files, mask_files):
        # Load image and mask, then preprocess (e.g., normalize pixel values)
        # Use appropriate image loading function depending on your data format (e.g., PIL, OpenCV, etc.)
        pass
    return np.array(images), np.array(masks)

def unet(input_size=(256, 256, 3)):
    inputs = Input(input_size)

    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    # Repeat this pattern for each encoding and decoding block, increasing the number of filters
    # For example: conv2, conv3, conv4, ..., up6, up7, up8

    conv9 = Conv2D(2, (3, 3), activation='relu', padding='same')(up8)
    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])

    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

    return model

# Load and preprocess your data
train_images, train_masks = load_and_preprocess_data()

# Create and compile the U-Net model
model = unet()

# Train the model
model.fit(train_images, train_masks, batch_size=32, epochs=100, validation_split=0.1)

# Save the model
model.save('unet_remote_sensing.h5')