遥感图像的语义分割，基于深度学习，在Tensorflow框架下，利用TF.Keras，运行环境TF2.0+.zip

#coding=utf-8 import matplotlib matplotlib.use("Agg") import tensorflow as tf import argparse import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D,MaxPooling2D,UpSampling2D,BatchNormalization,Reshape,Permute,Activation from tensorflow.keras.utils import to_categorical from tensorflow.keras.preprocessing.image import img_to_array from tensorflow.keras.callbacks import ModelCheckpoint from sklearn.preprocessing import LabelEncoder from PIL import Image import matplotlib.pyplot as plt import cv2 import random import os from tqdm import tqdm seed = 7 np.random.seed(seed) #设置图像大小 img_w = 32 img_h = 32 #分类 n_label=6 classes=[0.0,17.0,34.0,51.0,68.0,255.0] labelencoder = LabelEncoder() labelencoder.fit(classes) #训练批次和每次数据量 EPOCHS = 5 BS = 32 #图像最大值 divisor=255.0 #图像根路径 filepath ='C:\\Users\Administrator\Desktop\Project\src\\' #读取图片 def load_img(path, grayscale=False): if grayscale: img = cv2.imread(path,cv2.IMREAD_GRAYSCALE) else: img = cv2.imread(path) img = np.array(img,dtype="float") / divisor return img #获取训练数据和测试数据地址 def get_train_val(val_rate = 0.25): train_url = [] train_set = [] val_set = [] for pic in os.listdir(filepath + 'train'): train_url.append(pic) random.shuffle(train_url) total_num = len(train_url) val_num = int(val_rate * total_num) for i in range(len(train_url)): if i < val_num: val_set.append(train_url[i]) else: train_set.append(train_url[i]) return train_set,val_set # 生成训练数据 def generateData(batch_size,data=[]): while True: train_data = [] train_label = [] batch = 0 for i in (range(len(data))): url = data[i] batch += 1 img = load_img(filepath + 'train/' + url) img = img_to_array(img) train_data.append(img) label = load_img(filepath + 'label/' + url, grayscale=True) label = img_to_array(label).reshape((img_w * img_h,)) train_label.append(label) if batch % batch_size==0: train_data = np.array(train_data) train_label = np.array(train_label).flatten() #拍平 train_label = labelencoder.transform(train_label) train_label = to_categorical(train_label, num_classes=n_label) #编码输出便签 train_label = train_label.reshape((batch_size,img_w,img_h,n_label)) yield (train_data,train_label) train_data = [] train_label = [] batch = 0 #生成测试的数据 def generateValidData(batch_size,data=[]): while True: valid_data = [] valid_label = [] batch = 0 for i in (range(len(data))): url = data[i] batch += 1 img = load_img(filepath + 'train/' + url) img = img_to_array(img) valid_data.append(img) label = load_img(filepath + 'label/' + url, grayscale=True) label = img_to_array(label).reshape((img_w * img_h,)) valid_label.append(label) if batch % batch_size==0: valid_data = np.array(valid_data) valid_label = np.array(valid_label).flatten() valid_label = labelencoder.transform(valid_label) valid_label = to_categorical(valid_label, num_classes=n_label) valid_label = valid_label.reshape((batch_size,img_w,img_h,n_label)) yield (valid_data,valid_label) valid_data = [] valid_label = [] batch = 0 #定义模型-网络模型 def SegNet(): model = Sequential() #encoder model.add(Conv2D(64,(3,3),strides=(1,1),input_shape=(img_w,img_h,3),padding='same',activation='relu',data_format='channels_last')) model.add(BatchNormalization()) model.add(Conv2D(64,(3,3),strides=(1,1),padding='same',activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2,2))) #(128,128) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2,2))) #(64,64) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #(32,32) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #(16,16) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D(pool_size=(2, 2))) #(8,8) #decoder model.add(UpSampling2D(size=(2,2))) #(16,16) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) #(32,32) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) #(64,64) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) #(128,128) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu')) model.add(BatchNormalization()) model.add(UpSampling2D(size=(2, 2))) #(256,256) model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(img_w, img_h,3), padding='same', acti