There is various kind of remote sensing images: drone, aerial, satellite, multi-temporal, multi-spectral, … and each product has its advantages and weaknesses. What if we could synthesize a “super-resolution” sensor that could benefit from the advantages of all these image sources? For instance, what if we could fuse (1) high spatial resolution sensor (you know, the one that passes 1 time a year over your ROI), and (2) high temporal resolution sensor (…of which images can’t even let you identify where’s your house!) to derive one synthetic super sensor that has high spatial and high temporal resolution?
Today’s data-driven deep learning techniques establish state of the art results that would be crazy not to consider.
Super-resolution Generative Adversarial Networks
In their paper (“Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network“, which is quite a good read), Ledig et al. propose an approach to enhance low resolution images. They use Generative Adversarial Networks (GAN) to upsample and enhance a single low resolution image, such as the result is looking as the target (high resolution) image. GANs consist in two networks: the generator and the discriminator. In one hand, the generator transforms the input image (the low-res one) into an output image (the synthesized high-res one). In the other hand, the discriminator inputs two images (one low-res image, and one high-res image), and produce a signal that expresses the probability that the second image (the high-res) is a real one. The objective of the discriminator is to detect “fake” (i.e. synthesized) hi-res images among real ones. Simultaneously, the objective of the generator is to produce good looking hi-res images that fool the discriminator, and that are also close to the real hi-res images.
From images to remote sensing images
Now suppose we have some high res remote sensing images (say Spot 6/7, with 1.5m pixel spacing) and some low res remote sensing images (like Sentinel-2, with 10m pixel spacing) and we want to enhance the low res images at the same resolution of the high res. We can train our SRGAN generator over some patches and (try to…) make Sentinel-2 images look like Spot 6/7 images !
First I have downloaded a Sentinel-2 image from the Theia data center. The image is a monthly synthesis of multiple Sentinel-2 surface reflectance scenes produced with MAJA software (see here for details). The image is cloudless, thank to the amazing work of the CNES and CESBIO guys. I used a pansharpened Spot 6/7 scene acquired in the same month as the Sentinel-2 image, in the framework of the GEOSUD project.
The tools of choice to perform this are the OrfeoToolbox with OTBTF (a remote module that uses TensorFlow). First, we extract some patches in images (using otbcli_PatchesExtraction).
Once this is done, we can train our generator over these patches, using some SRGAN-inspired code (since we can find many SRGAN implementations on github!). After the model is trained properly, we can transform our Sentinel-2 scenes into images with 1.5m pixel spacing using the otbcli_TensorflowModelServe application (first time that I had to set a outputs.spcscale parameter lower than 1.0!). The synthesized high resolution Sentinel-2 image has a size of 72k by 72k.
Below are a couple of illustrations. Left: the original RGB Sentinel-2 image, Right: the enhanced RGB Sentinel-2 image. In a sake of sincerity, we put images that are far away from learning patches. You can enlarge them to full resolution.
We put the entire generated image in a cartographic server. You can browse the map freely (Select the “S2_RGB (High-Res)” from the layer menu, to display the 1.5m Sentinel-2 image). You can also display patches used for training (“Training patches footprint” layer).
Alternatively, you can use the following links if the above doesn’t work or is laggy, or if you want to see it fullscreen: link#1 or link#2 (just skip all warnings, it’s not dangerous!).
In this post we show what deep learning could offer in therm of data driven image enhancement for remote sensing images, at least for illustrative use.
In this tutorial, we will see how to train and apply a deep classifier on real world remote sensing images, using only user-oriented open-source software. No coding skills required!
Our dataset is composer as follow:
One remote sensing image, with coordinate reference system. The file is located in data/results/s6pxs_20150603_subset_norm.tif
Two vector layers (one for training and one for validation), composed of polygons. Each polygon has a “class” attributes that contain an integer value starting from 0 and describing the class. The files are located in data/vector/terrain_truth_A.shp and data/vector/terrain_truth_B.shp. We assume that there is 8 different classes.
We want to classify, meaning that we intend to estimate the class of each pixel of the remote sensing image.
For this we will use the OTBTF (see the blog post here) remote module of the Orfeo ToolBox (OTB), which rely on TensorFlow to perform numeric computations.
It is completely user-oriented, and you can use the provided applications in graphical user interface as any Orfeo ToolBox applications. Concepts introduced in OTBTF are presented in .
Deep learning backgrounds
To goal of this post is not to do a lecture about how deep learning works, but quickly summarizing the principle!
Deep learning refers to artificial neural networks with deep neuronal layers (i.e. a lot of layers !). Artificial neurons and edges typically have parameters that adjust as learning proceeds.
Weights modify the strength of the signal at a connection. Artificial neurons may output in non linear functions to break the linearity, for instance to make the signal sent only if the aggregate signal crosses a given threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times. Among common networks, Convolutional Neural Networks (CNN) achieve state of the art results on images. CNN are designed to extract features, enabling image recognition, object detection, semantic segmentation. A good review of deep learning techniques applied to remote sensing can be found here . In this tutorial, we focus only on CNN for a sake of simplicity.
During the following steps, we advise you to use QGIS to check generated geospatial data. Note that applications parameters are provided in the form of command line, but can be performed using the graphical user interface of OTB.
We can decompose the steps that we will perform as follow:
Sampling: we extract patches of images associated to the terrain truth,
Model training: we train the model from patches,
Apply the model to the full image: we generate a nice landcover map!
Sampling the remote sensing images
The first step to apply deep learning techniques to real world datasets is sampling. The existing framework of OTB offers great tools for pixel wise or object oriented classification/regression tasks. On the deep learning side, nets like CNNs are trained over patches of images rather than batches of single pixel. Hence the first application of OTBTF we will present targets the patches sampling and is called PatchesExtraction.
The PatchesExtraction application integrates seamlessly in the existing sampling framework of OTB : typically one can use the PolygoncClassStatistics and SampleSelection applications to select patches centers, then give them to the PatchesExtraction application. First, let’s compute some statistics of the input terrain truth with the PolygoncClassStatistics application.
field is the attribute that we will use as the label (i.e. the class),
out is a XML file that will be created, containing statistics of samples distribution (how many pixels in each class, and how many pixels in each polygons)
Open the generated file results/terrain_truth_A_stats.xml to check that statistics have been correctly computed from the input image and the vector data. Then, we use the SampleSelection application to select the physical position of samples. During this step, the application will use the previously computed statistics to perform efficiently the sample positions selection. There is many options to perform the selection, and you are free to chose the parameters you want (In the following, we select 2k samples for each class of the terrain_truth_A vector layer).
field is the attribute that we will use as the label (i.e. the class),
instats is the statistics XML file created previously,
strategy controls how the sampling will be done,
out is the output vector data, with only points as geometries (samples locations).
Side note on terrain truth split: For validation purposes, the terrain truth (terrain_truth_A and terrain_truth_B ) must consist in two vector layers such as the polygons of A do not overlap polygons of B. This way, we have two groups of polygons that are completely distinct. When we perform the sample selection, this guarantee that there is no selected position from A and B that come from the same polygon.
Repeat the previously described steps for the second vector layer terrain_truth_B.shp. Open the data in QGIS to check that the samples locations are correctly generated.
Now we should have two vector layers:
It’s time to use the PatchesExtraction application. The following operation consists in extracting patches in the input image, at each location of the terrain_truth_A_positions. In order to train a small CNN later, we will create a set of patches of dimension 16×16 associated to the corresponding label given from the class field of the vector data. Let’s do this :
source1 is first image source (it’s a parameter group),
source1.il is the input image list of the first source,
source1.patchsizex is the patch width of the first source,
source1.patchsizey is the patch height of the first source,
source1.out is the output patches image of the first source,
vec is the input vector data of the points (samples locations),
field is the attribute that we will use as the label value (i.e. the class),
outlabels is the output image for the labels.
After this step, you should have generated the following output images, that we will name “the training dataset”:
Trick to check the extracted patches from QGIS : Simply open the patches as raster layer, then chose the same coordinates reference system as the current project (indicated on the bottom right of the window).
Repeat the previously described steps for the vector layer terrain_truth_B_positions and generate the patches and labels for validation. After this step, you should have the following data that we will name “the validation dataset” :
Side note on pixel interleave: sampled patches are stored in one single big image that stacks all patches in rows. There is multiple advantages to this. First, accessing one unique big file is more efficient than working on thousands of separate small files stored in the file system. Secondly, one can visualize/process patches as any image : for instance, we can import the big patches image in QGIS and check that our patches looks good. Thirdly, the interleave of the sampled source is preserved, which, guarantee good performance during data access. Besides, we expect that a deep net use the same kind of input for training than for inference, in term of data interleave (it seems rational to consider that the data shouldn’t need to be reordered, for training or inference !).
How to train a deep net ? To begin with something easy, we will train a small existing model. We focus on a CNN that inputs our 16 × 16 × 4 patches and produce the prediction of an output class among 8 labels ranging from 0 to 7.
Here is a quick overview of some basic concepts about the training of deep learning models.
Model training usually involves a gradient descent (in the network weights space) of a loss function that typically expresses the gap between estimated and reference data. The batch size defines the number of samples that will be propagated through the network for this gradient descent. Supposing we have N training samples and we want to use a batch size of n. During learning, the first n samples (from 1 to n) will be used to train the network. Then, the second n samples (from n+1 to 2n) will be used to train the network again. This procedure is repeated until all samples are propagated through the network (this is called one epoch).
We propose to introduce a small and simple CNN to better understand the approach. This section describe what is inside this model. The figure below summarizes the computational graph of our CNN.
Input : The image patches fed the placeholder named “x” in the TensorFlow model. The first dimension of “x” might have any number of components. This dimension is usually employed for the batch size, and isn’t fixed to enable the use of different batch size. For instance, assuming we want to train our model with a batch of 10 samples, we will fed the model a multidimensional array of size 10 × 16 × 16 × 4 in the placeholder “x” of size # × 16 × 16 × 4.
Deep net : “x” is then processed by a succession of 2D-Convolution/Activation function (Rectified linear)/Pooling layer. After the last activation function, the features are processed by a fully connected layer of 8 neurons (one for each predicted class).
Predicted class : The predicted class is the index of the neuron (from the last neuron layer) that output the maximum value. This is performed in processing the outputs of the last fully connected layer with the Argmax operator, which is named “prediction” in the graph.
Cost function : The goal of the training is to minimize the cross-entropy between the data distribution (real labels) and the model distribution (estimated labels). We use the cross-entropy of the softmax of the 8 neurons as a cost function. In short, this will measure the probability error in the discrete classification task in which the classes are mutually exclusive (each entry is in exactly one class). For this, the model implements a function of TensorFlow know as Softmax cross entropy with logits. This function first computes the Softmax function of the 8 neurons outputs. The softmax function will normalize the output such as their sum is equal to 1 and can be used to represent a categorical distribution, i.e, a probability distribution over n different possible outcomes. The Shannon cross entropy between true labels and probability-like values from the softmax is then computed, and considered as the loss function of the deep net.
Optimizer : Regarding the training, a node called “optimizer” performs the gradient descent of the loss function : this node will be used only for training (or fine tuning) the model, it is useless to serve the model for inference ! The method implemented in this operator is called Adam (like “Adaptive moment estimation” ). A placeholder named “lr” controls the learning rate of the optimizer : it holds a single scalar value (floating point) and can have a default value.
Important note: The functions used in our model are working on labels that are integers ranging from 0 to N classes = 7. The first class number must be 0 to enforce the class numbering convention of the TensorFlow functions used in our model.
To generate this model, just copy-paste the following into a python script named python/create_savedmodel_simple_cnn.py
You can also skip this copy/paste step since this python script is located in the source code of OTBTF, here.
from tricks import *
# input patches: 16x16x4
conv1 = tf.layers.conv2d(inputs=x, filters=16, kernel_size=[5,5], padding="valid",
activation=tf.nn.relu) # out size: 12x12x16
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2) # out: 6x6x16
conv2 = tf.layers.conv2d(inputs=pool1, filters=16, kernel_size=[3,3], padding="valid",
activation=tf.nn.relu) # out size: 4x4x16
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) # out: 2x2x16
conv3 = tf.layers.conv2d(inputs=pool2, filters=32, kernel_size=[2,2], padding="valid",
activation=tf.nn.relu) # out size: 1x1x32
features = tf.reshape(conv3, shape=[-1, 32], name="features")
# 8 neurons for 8 classes
estimated = tf.layers.dense(inputs=features, units=nclasses, activation=None)
estimated_label = tf.argmax(estimated, 1, name="prediction")
return estimated, estimated_label
""" Main """
if len(sys.argv) != 2:
print("Usage : <output directory for SavedModel>")
# Create the TensorFlow graph
x = tf.placeholder(tf.float32, [None, None, None, 4], name="x")
y = tf.placeholder(tf.int32 , [None, None, None, 1], name="y")
lr = tf.placeholder_with_default(tf.constant(0.0002, dtype=tf.float32, shape=),
y_estimated, y_label = myModel(x)
# Loss function
cost = tf.losses.sparse_softmax_cross_entropy(labels=tf.reshape(y, [-1, 1]),
logits=tf.reshape(y_estimated, [-1, nclasses]))
optimizer = tf.train.AdamOptimizer(learning_rate=lr, name="optimizer").minimize(cost)
# Initializer, saver, session
init = tf.global_variables_initializer()
saver = tf.train.Saver( max_to_keep=20 )
sess = tf.Session()
# Create a SavedModel
CreateSavedModel(sess, ["x:0", "y:0"], ["features:0", "prediction:0"], sys.argv)
The imported tricks.py is part of the OTBTF remote module, and contains a set of useful functions and helpers to create SavedModel . It is located in the OTBTF source code. The environment variable PYTHONPATH must hence contain the path to this directory to enable the use of tricks.py in our script. Our python script uses the TensorFlow python API to generate the model, and serializes it as a SavedModel (google protobuf) written on disk.
If you take a look in the data/results/SavedModel_cnn directory, you will see a .pb file and a Variables folder. The protobuf file serializes the computational graph, and the Variables folder contains the values of the model weights (kernels, etc.). As you could have noticed in the python script, the model weights are initialized before exporting the SavedModel . We will use the TensorflowModelTrain application to train the CNN from its initialized state, updating its weights for the image classification task. For each dataset (training data and validation data), the validation step of the TensorflowModelTrain application consists in computing usual validation metrics.
Let’s quickly summarize the application parameters:
training.source1 is a parameter group for the patches source (for learning)
training.source1.il is the input image filename of the patches
training.source1.patchsizex is the patch width
training.source1.patchsizey is the patch height
training.source1.placeholder is the name of the placeholder for the patches
training.source2 is a parameter group the labels source (for learning)
training.source2.il is the input image filename of the labels
training.source2.patchsizex is the labels width
training.source2.patchsizey is the labels height
training.source2.placeholder is the name of the placeholder for the labels
model.dir is the directory containing the TensorFlow SavedModel
training.targetnodes is the name of the operator that we want to compute for the training step. In our model, the gradient descent is done with the adam optimizer node called “optimizer”.
validation.mode is the validation mode. The “class” validation mode enables the computation of classification metrics for bot training data and validation data.
validation.source1 is a parameter group for the patches source (for validation). As the name of the source for validation (validation.source1.name) is the same as the placeholder name of the same source for training (training.source1.placeholder), this source is considered as an input of the model, and is fed to the corresponding placeholder during the validation step.
validation.source2 is the labels source (for validation). As the name of the source (validation. source2.name) is different than the placeholder name of the same source for training (training.source2.placeholder), this source is considered as a reference to be compared to the output of the model that have the same tensor name during the validation step.
model.saveto enables to export the model variables (i.e. weights) to a file
The command line corresponding to the above description is the following:
Run the TensorflowModelTrain application. After the epochs, note the kappa and overall accuracy indexes (should be respectively around 0.64 and 0.68 over the validation dataset). Browse the file system, and take a look in the data/results directory : you can notice that the application has created two new files :
model_variables_cnn.index is a summary of the saved variables,
model_variables_cnn.data-00000-of-00001 is the first saved variable (saved at the end of the training process).
A quick comparison of scores versus Random Forest
Let’s use OTB to compare quickly the scores obtained using RF.
Here, we compare our tiny deep net to a Random Forest (RF) classifier. We use the metrics deriving from the confusion matrix. Let’s do it quickly thank to the machine learning framework of OTB First, we use the exact same samples as for the deep net training and validation, to extract the pixels values of the image :
Then, we do the same with the terrain_truth_B_positions.shp vector data (validation data). Finally, we train a RF model with default parameters:
# Train a Random Forest classifier with validation
-io.vd "data/results/terrain_truth_A_samples.shp" \
-valid.vd "data/results/terrain_truth_B_samples.shp" \
-feat "value_0" "value_1" "value_2" "value_3" \
-cfield "class" \
-classifier "rf" \
Compare the scores ! (With the author’s data, the kappa index is around 0.44, more than 20% less than our CNN model).
The metrics should be far better with CNN than RF: The CNN uses a lot more information for training, compared to the RF. It learns on 16 × 16 patches of multispectral image, whereas the RF learns only on multispectral pixels (that could be interpreted as a 1 × 1 patches of multispectral image). So let’s not say that CNN is better than RF! The goal here is not to compare RF vs CNN, but just to show that the contextual information process by the CNN is useful in classification task. A more fair competitor could be a RF using spatial features like textures, local Fourier transforms, SIFTs, etc.
Create a landcover map
Updating the CNN weights
As explained before, the TensorFlow model is serialized as a SavedModel , which is a bundle including the computational graph, and the model variables (kernel weights). We have previously trained our model from scratch : we have updated its variables from their initial state, and exported them on the file system. Now, we should update the model variables with the new ones. To do this, just copy/paste the new variables (cnn_model1.data-00000-of-00001) inside a copy of the model. You can do it using the file browser, or from command line.
Running the model
Let’s run the model over the remote sensing image to produce a nice land cover map! For this, we will use the TensorflowModelServe application. We know that our CNN input has a receptive field of 16 × 16 pixels and the placeholder name is “x”. The output of the model is the estimated class, that is, the tensor resulting of the Argmax operator, named “prediction”. Here, we won’t use the optimizer node as it’s part of the training procedure. We will use only the model as shown in the following figure:
As we might have no GPU support for now, it could be slow to process the whole image. We won’t produce the map over the entire image (even if that’s possible thank to the streaming mechanism) but just over a small subset. We do this using the extended filename of the output image, setting a subset starting at pixel 1000, 4000 with size 1000 × 1000. This extended filename consists in adding ?&box=1000 :4000 :1000 :1000 to the output image filename. Note that you can also generate a small image subset with the ExtractROI application of OTB, then use it as input of the TensorflowModelServe application.
source1 is a parameter group for the first image source,
source1.il is the input image list of the first source,
source1.rfieldx is the receptive field width of the first source,
source1.rfieldy is the receptive field height of the first source,
source1.placeholder is placeholder name corresponding to the the first source in the TensorFlow model,
model.dir is the directory of the SavedModel ,
output.names is the list of the output tensor that will be produced then generated as output image,
out is the output image generated from the TensorFlow model applied to the entire input image.
Now import the generated image in QGIS. You can change the style of the raster : in the layers panel (left side of the window), right-click on the image then select properties, go to the style tab and select render type as single band pseudocolor. Then, you can select the color for each class value, the annotations, etc.
Important note: We just have run the CNN in patch-based mode, meaning that the application extracts and process patches independently at regular intervals. This is costly, because the sampling strategy requires to duplicate a lot of overlapping patches, and process them independently. The TensorflowModelServe application enables to uses fully convolutional models, enabling to process seamlessly entire output images blocks... but that's another story!
This tutorials has explained how to perform an image classification using a simple deep learning architecture.
The OTBTF, a remote module of the Orfeo ToolBox (OTB), has been used to process images from a user’s perspective: no coding skills were required for this tutorial. QGIS was used for visualization purposes.
We will be at IGARSS 2019 for a full day tutorial extending this short introduction, and we hope to see you there!
 R. Cresson, A Framework for Remote Sensing Images Processing Using Deep Learning Techniques. IEEE Geoscience and Remote Sensing Letters, 16(1) :25-29, 2019.
 Liangpei Zhang, Lefei Zhang, and Bo Du. Deep learning for remote sensing data : A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2) :22–40, 2016.
 Diederik P Kingma and Jimmy Ba. Adam : A method for stochastic optimization. arXiv preprint arXiv :1412.6980, 2014.
Deep learning has proven to be an extremely powerful tool in many fields, and particularly in image processing: these approaches are currently subject of great interest in the Computer Vision community. However, while a number of typical CV problems can be directly transposed in Remote Sensing (Semantic labeling, Classification problems, …), some intrinsic properties of satellite and aerial imagery makes difficult to process real world RS images (large size, formats, geospatial characteristics, standards, etc.). That’s why pre-processed, pre-annotated public datasets like UC Merced or Postdam are so damn popular. And conversely, there is currently no operational, scalable, and user-friendly tools for people belonging to the RS community who have no coding knowledge.
In this post, we introduce a new remote module, otbtf, enabling the use of deep learning techniques with real world geospatial data. This remote module uses the high performance numerical computation library TensorFlow to bring the deep learning magic into OTB. The C++API of TensorFlow is used to run tensorflow sessions inside filters that are compliant with the streaming mechanism of ITK and OTB, meaning that there is no limitation on the size of images to be processed. Let’s see quickly what’s in the kit!
The first step to apply deep learning techniques to real world datasets, consists in building the dataset. The existing framework of the Orfeo ToolBox is great and offers tools like PolygoncClassStatistics, SampleSelection, SampleExtraction, and more recently SampleAugmentation. Those are really great for pixel wise or object oriented classification/regression tasks. On the deep learning side, among popular deep nets, the Convolutional Neural Network (CNN) has shown great potential for a number of tasks (segmentation, classification, object detection, image recognition, etc.). However, the CNN are trained over patches of images rather than batches of single pixel. Hence the first application of otbtf targets the patches sampling and is called (by the way) PatchesExtraction. It integrates seamlessly in the existing sampling framework of OTB: typically one can use the PolygoncClassStatistics and SampleSelection applications to select patches centers, then give them to the PatchesExtraction application. As we wanted to keep things simple, sampled patches are stored in one single big image that stacks all patches in rows . There is multiple advantages to this. First, accessing one unique big file is more efficient than working on thousands of separate small files stored in the file system. Secondly, one can visualize/process patches as any image: for instance, we can import the big patches image in QGIS and check that our patches looks good. Thirdly, the interleave of the sampled source is preserved, which, guarantee good performance during data access. Besides, we expect that a deep net use the same kind of input for training than for inference, in term of data interleave (it seems rational to consider that the data shouldn’t need to be reordered, for training or inference!).
Training a deep net
Once patches are extracted in images, one can train a deep net, feeding to the application TensorflowModelTrain some patches for training and patches for validation. We think that there will be more and more deep nets available for RS processing. Regarding TensorFlow, a convenient way to serialize models is done through Google Protobuf (called SavedModel). This enables the reuse of a model, for training or inference. The TensorflowModelTrain loads a SavedModel, then train it. As the model variables can be either loaded or saved, one can train the model from scratch or perform some fine tuning, and save variables at different places allowing to process the same model later with different kernel weights, etc (e.g. in classification task, we can train the model from scratch for an entire country, then performing some fine tuning over multiple ecological region).
Currently, we are working on the integration of something to make TensorflowModelTrain reports to TensorBoard. Also, we would add something in a near future to optimize the streaming of batches during training/validation. We will also create soon a repository somewhere to host various SavedModel implemented in research papers.
Serving a deep net
This is where thing become interesting. With the TensorflowModelServe application, we can use any tensorflow model with any number of input sources, any number of input placeholders (that might as well be some user-specific scalar placeholders, for instance “parameter1=0.2”). Thank to the streaming mechanism, we can process any number of pixels in a seamless way. There is two modes to use a deep net: (1) Patch-based: extract and process patches independently at regular intervals. Patches sizes are equal to the receptive field sizes of inputs. For each input, a tensor with a number of elements equal to the number of patches feds the TensorFlow model. (2) Fully-convolutional: Unlike patch-based mode, it allows the processing of an entire requested region. For each input, a tensor composed of one single element, corresponding to the input requested region, is fed to the TensorFlow model. This mode requires that receptive fields, expression fields (the output space that a model will produce for one input patch) and scale factors (the total stride between the input and the output, i.e. the physical spacing change) are consistent with operators implemented in the TensorFlow model, input images physical spacing and alignment. This second mode enables even “old” hardware to process entire scenes in reasonable processing time, enforcing the OTB philosophy 😉 Last but not least, as the entire pipeline implemented in TensorflowModelServe is fully streamable, the application benefits from the Message Passing Interface (MPI) support of OTB, and can be used on any High Performance Computing clusters that share parallel file system.
Using deep features inside OTB machine learning framework
Recent studies have suggested that deep nets features can be used as input features of algorithms like classification, leading state of the art results. Regarding RS image classification, OTB already implement a number of algorithms in its classification application, e.g. SVM, Random Forests, boost classifier, decision tree classifier, gradient boosted tree classifier, normal Bayes classifier. We provide two composite application, TrainClassifierFromDeepFeatures and ImageClassifierFromDeepFeatures, that reuse TensorflowModelServe as input of, respectively the TrainImagesClassifier and ImagesClassifier applications (official OTB applications dedicated to train a classifier from features and perform a pixel wise image classification). We could also do the same thing for SampleExtractionFromDeepFeatures, TrainVectorClassifier to name a few (future work!).
Now, you don’t have any excuse to apply leading, state of the art classification methods 😉
You can read this paper to have more details about concepts involved in otbtf.