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s2cnnclass.ipynb
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| {"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"s2cnnclass.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"}},"cells":[{"cell_type":"markdown","metadata":{"id":"8QZMxH4E2Qj7"},"source":["# Sentinel-2 image classification with Convolutional Neural Networks\n","## Mort Canty\n","## ZFL Bonn, March, 2021"]},{"cell_type":"markdown","metadata":{"id":"5rJN97BoaEGK"},"source":["## Context\n","Image recognition with deep learning techniques such as the convolutional neural network (CNN) relies heavily on the availablity of vast amounts of labeled training data as well as a lot of computing resources. For typical remote sensing image classification these are usually not available. We'll explore an alternative approach which makes use of pre-trained networks, freely available Sentinel-2 imagery, the EuroSAT Sentinel-2 training database and TensorFlow.\n"," \n","### References\n","[Géron (2019) Hands-On Machine Learning ...](https://b-ok.cc/book/5341845/f49201)\n"," \n","[Helber et al. (2017) EuroSAT: A novel dataset and deep learning benchmark ...](https://www.researchgate.net/publication/319463676_EuroSAT_A_Novel_Dataset_and_Deep_Learning_Benchmark_for_Land_Use_and_Land_Cover_Classification)\n","\n","[Leitloff (2018) github.com/jensleitloff/CNN-Sentinel](https://github.com/jensleitloff/CNN-Sentinel)\n","\n","[Sumbul et al. (2020) BigEarthNet: Dataset with a new class-nomenclature for remote sensing image understanding](https://arxiv.org/abs/2001.06372)\n","\n","### Software\n","\n","[Python scripts](https://drive.google.com/drive/folders/1D2RAEu14Zcl9NzqWkSg4J7MilHGHi0BH?usp=sharing)"]},{"cell_type":"markdown","metadata":{"id":"UMYv3ICRdMwS"},"source":["## Preliminaries"]},{"cell_type":"code","metadata":{"id":"EkILT8AAwiOq"},"source":["import numpy as np\n","import tensorflow as tf\n","import tensorflow.keras as keras\n","import tensorflow_datasets as tfds\n","import matplotlib.pyplot as plt\n","from google.colab import drive, files\n","import IPython.display as disp\n"," \n","%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"85Xux7Nu8QWi"},"source":["import ee\n","ee.Authenticate()\n","\n","ee.Initialize()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"F7UKmTCe-Pjf"},"source":["drive.mount('/content/drive')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QKc0QFSc-clq"},"source":["!ls /content/drive/MyDrive/scripts"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"hpyBGnXD3FMf"},"source":["## 1. The fashion/mnist dataset\n","\n","We begin with a simple \"hello world\" type example to get familiar with convolutional networks:\n","\n","MNIST: Modified National Institute of Standards and Technology dataset\n","\n","https://medium.com/tensorflow/hello-deep-learning-fashion-mnist-with-keras-50fcff8cd74a"]},{"cell_type":"markdown","metadata":{"id":"PxZH3Zix5E5y"},"source":["Load the fashion-mnist dataset with the keras datasets model.\n","\n","\"*The tf.keras.datasets module provides a few toy datasets (already-vectorized, in Numpy format) that can be used for debugging a model or creating simple code examples.*\""]},{"cell_type":"code","metadata":{"id":"C0MEo3MJ5PK5"},"source":["(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()\n","print(\"x_train shape:\", x_train.shape, \"y_train shape:\", y_train.shape)\n","\n","# normalize the train/test data\n","x_train = x_train.astype('float32') / 255\n","x_test = x_test.astype('float32') / 255\n","\n","plt.imshow(x_test[55],cmap='gray',vmin=0,vmax=1)\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ObOf-4iz5p54"},"source":["# Further break training data into train / validation sets \n","# put 5000 into validation set and keep remaining 55,000 for train\n","(x_train, x_valid) = x_train[5000:], x_train[:5000] \n","(y_train, y_valid) = y_train[5000:], y_train[:5000]\n","\n","# Reshape input data from (28, 28) to (28, 28, 1)\n","w, h = 28, 28\n","x_train = x_train.reshape(x_train.shape[0], w, h, 1)\n","x_valid = x_valid.reshape(x_valid.shape[0], w, h, 1)\n","x_test = x_test.reshape(x_test.shape[0], w, h, 1)\n","\n","# One-hot encode the labels\n","y_train = tf.keras.utils.to_categorical(y_train, 10)\n","y_valid = tf.keras.utils.to_categorical(y_valid, 10)\n","y_test = tf.keras.utils.to_categorical(y_test, 10)\n","\n","# Print training set shape\n","print(\"x_train shape:\", x_train.shape, \"y_train shape:\", y_train.shape)\n","\n","# Print the number of training, validation, and test datasets\n","print(x_train.shape[0], 'train set')\n","print(x_valid.shape[0], 'validation set')\n","print(x_test.shape[0], 'test set')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AIme3k1v9FjP"},"source":["#### Build the model \n","https://towardsdatascience.com/mnist-handwritten-digits-classification-using-a-convolutional-neural-network-cnn-af5fafbc35e9\n","\n",""]},{"cell_type":"markdown","metadata":{"id":"kAVcPA6Silpv"},"source":["### TensorFlow high level sequential API"]},{"cell_type":"code","metadata":{"id":"v52uJPcb-kmz"},"source":["# Use the sequential API\n","model = tf.keras.Sequential()\n","# Must define the input shape in the first layer of the neural network\n","model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', \n"," input_shape=(28,28,1))) \n","model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n","model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))\n","model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n","model.add(tf.keras.layers.Flatten())\n","model.add(tf.keras.layers.Dense(128, activation='relu'))\n","model.add(tf.keras.layers.Dense(10, activation='softmax'))\n","# Take a look at the model summary\n","model.summary()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"O259IRqgfj8A"},"source":["keras.utils.plot_model(model, \"mnist_model.png\",show_shapes=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FwztPZhZ-vWC"},"source":["# conv2d: (3 times 3 pixel inputs + 1 bias) times 32 filters\n","conv2d1 = (3*3 + 1)*32\n","print('conv2d1 %i'%conv2d1)\n","\n","# conv2d: (3 times 3 pixel inputs times 32 filters + 1 bias) times 64 filters\n","conv2d2 = (3*3*32 + 1)*64\n","print('conv2d2 %i'%conv2d2)\n","\n","# flatten: 7*7*64\n","flatten = 7*7*64\n","print('flatten %i'%flatten)\n","\n","# dense (flatten inputs + 1 bias) times 128 neurons \n","dense1 = (flatten + 1)*128\n","print('dense1 %i'%dense1)\n","\n","# dense (128 inputs + 1 bias) times 10\n","dense2 = (128+1)*10\n","print('dense2 %i'%dense2)\n","\n","print('trainable parameters %i'%(conv2d1+conv2d2+dense1+dense2))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ju8mJS_j-0qy"},"source":["# summarize feature map shapes\n","for i in range(len(model.layers)):\n"," layer = model.layers[i]\n"," # check for convolutional layer\n"," if 'conv' not in layer.name:\n"," continue\n"," # summarize output shape\n"," print(i, layer.name, layer.output.shape)\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cHshcTRJSAzS"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"OWP_tW84-7wS"},"source":["#### Compile the model"]},{"cell_type":"code","metadata":{"id":"BnMPjQcj-6PE"},"source":["model.compile(loss='categorical_crossentropy',\n"," optimizer='adam',\n"," metrics=['accuracy'])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"toBRQkXW_U_V"},"source":["#### Train the model"]},{"cell_type":"code","metadata":{"id":"l6BxEk2z_TY-"},"source":["%%time\n","from tensorflow.keras.callbacks import ModelCheckpoint,TensorBoard\n","from datetime import datetime\n","now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n","logdir = 'tf_cnn/run-'+str(now)\n","# default: save checkpoint after each epoch\n","checkpointer = ModelCheckpoint(filepath='model.weights.best.hdf5', verbose = 1, save_best_only=True)\n","tensorboardlog = TensorBoard(logdir)\n","\n","model.fit(x_train,\n"," y_train,\n"," batch_size=64,\n"," epochs=10,\n"," validation_data=(x_valid, y_valid),\n"," callbacks=[checkpointer,tensorboardlog])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OE6GsKVNA4J1"},"source":["%load_ext tensorboard\n","%tensorboard --logdir tf_cnn"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Dxmz1zIjFMEX"},"source":["#### Evaluate the model"]},{"cell_type":"code","metadata":{"id":"5V5jF21cFDow"},"source":["# Load the weights with the best validation accuracy\n","model.load_weights('model.weights.best.hdf5')\n","\n","# Evaluate the model on test set\n","score = model.evaluate(x_test, y_test, verbose=0)\n","\n","# Print test accuracy\n","print( '\\nTest accuracy:', score )"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Ygp2mCkqFedJ"},"source":["#### Dig into the model\n","https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/"]},{"cell_type":"code","metadata":{"id":"6DU64AejFr75"},"source":["# redefine model to output right after the first pooling layer\n","model1 = tf.keras.Model(inputs=model.inputs, outputs=model.layers[1].output)\n","\n","x = x_train[0].reshape(1,28,28,1)\n","\n","featuremap = model1.predict(x)\n","featuremap.shape"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"eldDW_vxF75D"},"source":["# plot all 32 maps \n","ix = 1\n","for _ in range(4):\n"," for _ in range(8):\n"," # specify subplot and turn of axis\n"," ax = plt.subplot(4, 8, ix)\n"," ax.set_xticks([])\n"," ax.set_yticks([])\n"," # plot filter channel in grayscale\n"," plt.imshow(featuremap[0, :, :, ix-1], cmap='gray')\n"," ix += 1\n","# show the figure\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bjOiJpWfGELA"},"source":["# redefine model to output right after the second pooling layer\n","model2 = tf.keras.Model(inputs=model.inputs, outputs=model.layers[3].output)\n","\n","featuremap = model2.predict(x)\n","featuremap.shape"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GfNKsmy_GNpT"},"source":["# plot first 32 maps \n","ix = 1\n","for _ in range(4):\n"," for _ in range(8):\n"," # specify subplot and turn of axis\n"," ax = plt.subplot(4, 8, ix)\n"," ax.set_xticks([])\n"," ax.set_yticks([])\n"," # plot filter channel in grayscale\n"," plt.imshow(featuremap[0, :, :, ix-1], cmap='gray')\n"," ix += 1\n","# show the figure\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"H5FA1J4hwiOo"},"source":["## 2. Transfer Learning with the EuroSAT/rgb dataset\n","Transfer Learning is a deep learning technique whereby a model developed for a task is reused as the initial point for a model in another domain. Rather than training the model from scratch, the pre-trained model is modified, partially retrained and re-used. "]},{"cell_type":"markdown","metadata":{"id":"yypIvf5SsAa8"},"source":["### EUROSAT dataset (27000 Sentinel-2 RGB 64x64 images with labels)\n","\n","This dataset is part of the TFDS project. We use it to download the EuroSAT database and split it into test, train and validate subsets."]},{"cell_type":"code","metadata":{"id":"cppLiZH7wiOr"},"source":["(test_set, valid_set, train_set), info = tfds.load('eurosat/rgb', split = ['train[:10%]', 'train[10%:25]', 'train[25%:]' ], as_supervised=True, with_info=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pLS8k01QBVNr"},"source":["info.description"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NMon_RHQBAEP"},"source":["info.features"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"buY_uc4EB7bm"},"source":["info.features['label'].names"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LYl3MQinPLGD"},"source":["train_set"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"z5ZhVA3lTTvR"},"source":["fig=tfds.show_examples(train_set, info, rows=4, cols=4)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ooeWKs7GtTkD"},"source":["### Preprocess for Xception cnn\n","We will use the [Xception CNN](https://keras.io/api/applications/xception/) which has been pre-trained with the [IMAGENET](http://image-net.org/) database. The CNN requires image patches with dimension 224x224 and has its own preprocessor:"]},{"cell_type":"code","metadata":{"id":"Jh7SojXbwiOw"},"source":["def preprocess(image,label):\n"," resized_image = tf.image.resize(image, [224,224])\n"," final_image = keras.applications.xception.preprocess_input(resized_image)\n"," return final_image, label\n","\n","batch_size = 32\n","# Randomly shuffle the elements of the training dataset.\n","train_set = train_set.shuffle(1000)\n","# map the preprocessing function over the three datasets\n","train_set = train_set.map(preprocess).batch(batch_size).prefetch(1)\n","valid_set = valid_set.map(preprocess).batch(batch_size).prefetch(1)\n","test_set = test_set.map(preprocess).batch(batch_size).prefetch(1)\n","\n","test_set"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YL-KI3eyuAWO"},"source":["For a GPU runtime, the _prefetch_ method makes the CPU prepare the next batch while the GPU is processing the current one.\n","\n","### Setup the pre-trained xception model using weights obtained for the IMAGENET dataset\n","\n","So far we have built our networks with the Keras sequential API. Here we use the more flexible _functional API_."]},{"cell_type":"code","metadata":{"id":"QJ-ck6qswiOx"},"source":["# download xception (base) model without top layers\n","base_model = keras.applications.xception.Xception(weights='imagenet', include_top=False, input_shape=(224,224,3))\n","# add a pooling layer with input from the base model (averages over all output feature maps of the base model)\n","avg = keras.layers.GlobalAveragePooling2D()(base_model.output)\n","# add an output softmax layer with 10 neurons and softmax activation\n","output = keras.layers.Dense(10, activation='softmax')(avg)\n","# define the model\n","model = keras.Model(inputs=base_model.input, outputs=output)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RoKRCY49gDgO"},"source":["keras.utils.plot_model(model, \"eurosat_model.png\", dpi=48, show_shapes=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BLbP2Ww_vd98"},"source":["Freeze the layers in the base model"]},{"cell_type":"code","metadata":{"id":"XZknAi1swiOx"},"source":["for layer in base_model.layers:\n"," layer.trainable = False"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bHX0hrVPvv7_"},"source":["Define optimization method and compile the model"]},{"cell_type":"code","metadata":{"id":"AA0ncLYuwiOx"},"source":["# scaled gradient descent optimizer\n","optimizer = keras.optimizers.SGD(lr=0.2, momentum=0.9, decay=0.01)\n","# cross entropy loss (for no one-hot labels encoding)\n","loss = \"sparse_categorical_crossentropy\"\n","# only display accuracy = overall correct / total examples\n","metrics = ['accuracy']\n","\n","model.compile(loss=loss, optimizer=optimizer, metrics=metrics)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"10XeLpSxv6uZ"},"source":["Train the model, top layer weights only (use GPU runtime!)"]},{"cell_type":"code","metadata":{"id":"ww-rh-SBwiOy","scrolled":false},"source":["history = model.fit(train_set, epochs=10, validation_data=valid_set)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PrqCGhcswn5l"},"source":["Fine tune the rest of the weights"]},{"cell_type":"code","metadata":{"id":"HAqxpYWKwiOy"},"source":["for layer in base_model.layers:\n"," layer.trainable = True\n","optimizer = keras.optimizers.SGD(lr=0.01, momentum=0.9, decay=0.001)\n","model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer, metrics=['accuracy']) \n","history = model.fit(train_set, epochs=5, validation_data=valid_set)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"uaLudiLZymyQ"},"source":["Test (on first 32 test examples)"]},{"cell_type":"code","metadata":{"id":"rKzuN9E-yhDl"},"source":["print( np.argmax(model.predict(test_set.take(1)),1) )\n","for image, label in test_set.take(1): \n"," print(np.array(label))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EDIkLl2iPXvj"},"source":["model.evaluate(test_set)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_Hn5O2XyECqb"},"source":[" Save the model"]},{"cell_type":"code","metadata":{"id":"tsr6P_L-gRDk"},"source":["model.save('/content/drive/MyDrive/gee/eurosat_model.h5')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lQxiRQ3Wf5OC"},"source":["from google.colab import files\n","files.download('/content/drive/MyDrive/gee/eurosat_model.h5')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9GB1m_LKHIap"},"source":["Export a Sentinel-2 image to Google Drive"]},{"cell_type":"code","metadata":{"id":"-qQjm4MVH1C2"},"source":["geoJSON = {\n"," \"type\": \"FeatureCollection\",\n"," \"features\": [\n"," {\n"," \"type\": \"Feature\",\n"," \"properties\": {},\n"," \"geometry\": {\n"," \"type\": \"Polygon\",\n"," \"coordinates\": [\n"," [\n"," [\n"," 5.9710693359375,\n"," 50.47848271564207\n"," ],\n"," [\n"," 7.2125244140625,\n"," 50.47848271564207\n"," ],\n"," [\n"," 7.2125244140625,\n"," 51.156954270151346\n"," ],\n"," [\n"," 5.9710693359375,\n"," 51.156954270151346\n"," ],\n"," [\n"," 5.9710693359375,\n"," 50.47848271564207\n"," ]\n"," ]\n"," ]\n"," }\n"," }\n"," ]\n","}\n","\n","coords = geoJSON['features'][0]['geometry']['coordinates']\n","aoi = ee.Geometry.Polygon(coords)\n","\n","s2 = ee.ImageCollection('COPERNICUS/S2') \\\n"," .filterBounds(aoi) \\\n"," .filterDate(ee.Date('2018-05-07'),ee.Date('2018-05-09')) \\\n"," .filter(ee.Filter.contains(rightValue=aoi,leftField='.geo')) \\\n"," .first() \\\n"," .select('B2','B3','B4')\n","\n","gdexport = ee.batch.Export.image.toDrive(s2.clip(aoi),\n"," description='driveExportTask_s2', \n"," folder = 'gee',\n"," fileNamePrefix = 's2_20180508',\n"," scale = 10,\n"," maxPixels = 1e11)\n","gdexport.start()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"C4ePIXf-sXoK"},"source":["run /content/drive/MyDrive/scripts/dispms -f /content/drive/MyDrive/gee/s2_20180508.tif -e 4 -p [1,2,3] -d [500,500,4000,4000]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kA4KNE7qrK8l"},"source":["The classification is carried out by a Python script S2cnnclassify.py using the saved model:"]},{"cell_type":"code","metadata":{"id":"BJBU-jhQZgbj"},"source":["%run /content/drive/MyDrive/scripts/S2cnnclassify -d [500,500,4000,4000] -s 8 /content/drive/MyDrive/gee/s2_20180508.tif "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bnh_pnlunawU"},"source":["In order to visualize the result it is convenient to view it in the GEE code editor (download from Drive and upload to assets).\n","\n","https://code.earthengine.google.com/b00ca86e09a78b0970ef41b9582b7665"]},{"cell_type":"markdown","metadata":{"id":"FcZJP8psLXXW"},"source":["### Project\n","The 10 target classes should be seasonally invariant. Try to determine if this is the case."]},{"cell_type":"markdown","metadata":{"id":"hfxWYOFCvUM2"},"source":["### Outlook\n","In the third and final part of the course we will return to analysis of time series of SAR imagery and examine a sequential change detection method. The Colab notebooks are on the [Earthengine community tutorial website.](https://developers.google.com/earth-engine/tutorials/community/detecting-changes-in-sentinel-1-imagery-pt-1)"]}]} |
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It is shared. You can use this link
https://code.earthengine.google.com/1ad78e6c99d94eac5c5129eaa33a9601
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Thanks for sharing!
However, there's a small problem with access to the dataset that prevents us from viewing results:
Line 5: Image.load: Image asset 'users/mortcanty/s2_20180508_optical_cnn' not found.Could you provide a read-all access to this image?