Unet是一种U型网络,分为左右两部分卷积,左边为下采样提取高维特征,右边为上采样并与左侧融合实现图像分割。这里使用TensorFlow实现Unet网络,实现对遥感影像的道路分割。
训练数据:

标签图像:
Unet实现:
import tensorflow as tf
import numpy as np
import cv2
import glob
import itertools
class UNet:
def __init__(
self,
input_width,
input_height,
num_classes,
train_images,
train_instances,
val_images,
val_instances,
epochs,
lr,
lr_decay,
batch_size,
save_path
):
self.input_width = input_width
self.input_height = input_height
self.num_classes = num_classes
self.train_images = train_images
self.train_instances = train_instances
self.val_images = val_images
self.val_instances = val_instances
self.epochs = epochs
self.lr = lr
self.lr_decay = lr_decay
self.batch_size = batch_size
self.save_path = save_path
def leftNetwork(self, inputs):
x = tf.keras.layers.Conv2D(64, (3, 3), padding='valid', activation='relu')(inputs)
o_1 = tf.keras.layers.Conv2D(64, (3, 3), padding='valid', activation='relu')(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2,2), strides=(2, 2))(o_1)
x = tf

本文介绍了如何使用TensorFlow构建Unet模型,用于遥感图像中的道路分割任务。通过左半部分的下采样提取高维特征,右半部分的上采样融合实现精确道路与背景区分,展示了从数据预处理到模型训练及推理的完整流程。
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