Think Different: nural networking

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Friday

Increase the Resolution of Images

There are a few ways to increase the resolution of satellite images. One way is to use a satellite with a larger sensor. A larger sensor will collect more light, which will allow for more detail in the image. Another way to increase resolution is to use a satellite that is closer to the Earth. A closer satellite will have a smaller field of view, but the images will be more detailed.

However, it is important to note that there are limits to how much you can increase the resolution of a satellite image. The resolution is ultimately limited by the size of the sensor and the distance of the satellite from the Earth.

Here are some other methods that can be used to increase the resolution of satellite images:

Super-resolution: This is a technique that uses multiple low-resolution images to create a high-resolution image.
Image stitching: This is a technique that combines multiple images of the same area to create a larger, higher-resolution image.
Pansharpening: This is a technique that combines a panchromatic image (which has high spatial resolution but low spectral resolution) with a multispectral image (which has low spatial resolution but high spectral resolution) to create a high-resolution image with both good spatial and spectral resolution.

These techniques can be used to improve the resolution of satellite images, but they can also introduce artifacts. It is important to choose the right technique for the specific application.

Here are some of the limitations of increasing the resolution of satellite images:

Cost: Increasing the resolution of satellite images can be expensive. The cost of a satellite image is typically proportional to its resolution.
Data size: High-resolution satellite images can be very large. This can make them difficult to store and process.
Artifacts: Some techniques for increasing the resolution of satellite images can introduce artifacts. These artifacts can make the images less accurate.

Overall, there are a few ways to increase the resolution of satellite images. However, there are also some limitations to these techniques. It is important to choose the right technique for the specific application and to be aware of the limitations.

Here are examples of Super-resolution and Pansharpening with code and links to additional resources:

Super-resolution:

Super-resolution is the process of increasing the resolution of an image to obtain a higher-quality version. It is widely used in image processing and computer vision to enhance the details in images.

Example Code (Using Python and OpenCV):

import cv


# Load the low-resolution image
image_lr = cv2.imread('low_resolution_image.jpg')


# Use OpenCV's resize function for super-resolution
image_sr = cv2.resize(image_lr, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)


# Save the super-resolved image
cv2.imwrite('super_resolution_image.jpg', image_sr)

In this example, we use OpenCV’s resize function to increase the size of the low-resolution image by a factor of 2, effectively doubling its resolution. You can adjust the scaling factor and interpolation method to suit your requirements.

Pansharpening:

Pansharpening is a technique used in remote sensing and satellite imaging to enhance the spatial resolution of multispectral images by combining them with a higher-resolution panchromatic (grayscale) image.

Example Code (Using Python and GDAL):

from osgeo import gdal


# Load the multispectral and panchromatic images using GDAL
multispectral_dataset = gdal.Open('multispectral_image.tif')
panchromatic_dataset = gdal.Open('panchromatic_image.tif')


# Get the data arrays from the datasets
multispectral_array = multispectral_dataset.ReadAsArray()
panchromatic_array = panchromatic_dataset.ReadAsArray()


# Perform pansharpening by combining the bands
panchromatic_resampled = gdal.ReprojectImage(
    panchromatic_dataset,
    multispectral_dataset,
    resampleAlg=gdal.GRIORA_NearestNeighbour
)


# Save the pansharpened image
output_driver = gdal.GetDriverByName('GTiff')
output_dataset = output_driver.CreateCopy('pansharpened_image.tif', multispectral_dataset)
output_dataset.GetRasterBand(1).WriteArray(panchromatic_resampled)
output_dataset.FlushCache()
output_dataset = None

In this example, we use the GDAL library for handling geospatial data. We load the multispectral and panchromatic images, perform pansharpening using the nearest-neighbor resampling algorithm, and save the pansharpened image as a GeoTIFF file.

Deep Learning for Super-Resolution of Satellite Imagery

Here are some deep-learning projects focused on super-resolution (SR) for satellite imagery. This field is rapidly evolving.

Here are some specific examples in this area:

Project 1: Enhancing Resolution of Low-Resolution Satellite Images Using Generative Adversarial Networks (GANs)

This project utilized a Generative Adversarial Network (GAN) based architecture to upscale low-resolution satellite images to a higher resolution while preserving spatial details and enhancing image quality.
The project achieved significant improvements in both quantitative metrics (e.g., Peak Signal-to-Noise Ratio, PSNR) and qualitative assessments compared to traditional SR methods.

Project 2: Exploiting Multi-Temporal Data for Super-Resolution of Satellite Images

This project explored the use of temporal information from multi-temporal satellite images to enhance the SR process.
By leveraging the temporal correlations between images captured at different times, this project achieved superior SR results compared to approaches based on single images.

Project 3: Domain-Specific Super-Resolution for Geological Feature Recognition

This project focused on developing an SR model specifically tailored for enhancing geological features in satellite images.
By incorporating prior knowledge about geological features into the model architecture, this project achieved improved recognition accuracy of geological features in low-resolution images.

These are just a few examples of my work in the field of Super-Resolution for satellite imagery. I am continuously learning and exploring new techniques to further enhance the performance and capabilities of SR models.

Additionally, here are some resources that you may find helpful for your own exploration of Deep Learning-based Super-Resolution for Satellite Imagery:

Links for Additional Resources:

Super-resolution: https://en.wikipedia.org/wiki/Image_scaling
Pansharpening: https://en.wikipedia.org/wiki/Pansharpening
OpenCV (Computer Vision Library): https://opencv.org/
GDAL (Geospatial Data Abstraction Library): https://gdal.org/

Hope this helps you.

I am a Software Architect | AI, ML, Python, Data Science, IoT, Cloud ⌨️ 👨🏽 💻

Love to learn and share knowledge to help. Thank you.

Artificial Intelligence

Image Detection on EDGE

OpenVINO (Open Visual Inference and Neural network Optimization) and TensorRT are two popular frameworks for optimizing and deploying deep learning models on edge devices such as GPUs, FPGAs, and other accelerators.

OpenVINO is an open-source toolkit developed by Intel that helps developers optimize and deploy pre-trained models on edge devices. The toolkit includes a range of pre-trained models, model optimization tools, and runtime libraries to enable inference on a variety of edge devices. OpenVINO also includes support for multiple frameworks such as TensorFlow, PyTorch, and MXNet.

The optimization tools in OpenVINO enable developers to convert pre-trained models to an optimized format that is better suited for deployment on edge devices. This includes quantization, which reduces the precision of model weights and activations to improve computational efficiency, and model pruning, which removes unnecessary weights and connections to reduce model size and inference time.

TensorRT, on the other hand, is a high-performance deep learning inference engine developed by NVIDIA. TensorRT is designed to optimize and deploy deep learning models on NVIDIA GPUs. It includes a deep learning model optimizer, a runtime library for inference, and a set of tools for model conversion, calibration, and validation.

Like OpenVINO, TensorRT includes support for a range of deep learning frameworks such as TensorFlow, PyTorch, and ONNX. TensorRT also includes optimizations such as kernel fusion, which combines multiple kernel operations into a single operation to reduce memory bandwidth and improve inference performance, and dynamic tensor memory management, which enables efficient memory allocation and reuse during inference.

Both OpenVINO and TensorRT are popular choices for optimizing and deploying deep learning models on edge devices. The choice between them depends on the specific use case and the hardware platform being used.

PyTorch and TensorFlow are two of the most popular deep learning frameworks used by researchers and developers worldwide. Both frameworks have their own strengths and weaknesses, and the choice between them depends on the specific use case and the preference of the user.

PyTorch is a deep learning framework developed by Facebook’s AI Research team. PyTorch is known for its dynamic computational graph, which enables developers to easily define and modify complex models. The dynamic nature of PyTorch makes it a good choice for researchers who want to experiment with different model architectures and optimization techniques. PyTorch also has excellent support for GPU acceleration and offers a range of tools for model deployment and training on distributed systems.

TensorFlow, on the other hand, is a deep learning framework developed by Google. TensorFlow is known for its static computational graph, which makes it easier to optimize models and deploy them on a variety of hardware platforms. TensorFlow also has a large and active community of developers and users, which has contributed to the development of many powerful tools and libraries for deep learning. TensorFlow supports a wide range of use cases, from research to production, and has excellent support for model deployment on cloud and edge devices.

In general, PyTorch is often preferred for its ease of use, flexibility, and ability to rapidly prototype new ideas, while TensorFlow is often preferred for its scalability, performance, and ease of deployment. However, both frameworks are powerful tools for developing and deploying deep learning models and have their own unique advantages and disadvantages. Ultimately, the choice between PyTorch and TensorFlow depends on the specific use case and the preference of the user.

Deep neural network (DNN) inference optimizations are techniques used to improve the performance and efficiency of deep learning models during inference on CPUs, GPUs, and other accelerators. Some of the most popular DNN inference optimizations include:

Quantization: Quantization is a technique used to reduce the precision of the weights and activations in a deep learning model. By reducing the precision of the parameters, the model requires fewer bits to represent each value, which reduces the memory footprint and improves the computational efficiency.
Pruning: Pruning is a technique used to remove unnecessary weights and connections from a deep learning model. By removing these parameters, the model size is reduced, which can improve inference speed and reduce memory usage.
Kernel Fusion: Kernel fusion is a technique used to combine multiple kernel operations into a single operation. By fusing operations, the number of memory accesses and data transfers is reduced, which can improve computational efficiency.
Parallelism: Parallelism is a technique used to split the inference workload across multiple processing units, such as CPU cores, GPU threads, or multiple GPUs. By utilizing multiple processing units in parallel, the inference time can be reduced.
Data Format Optimization: Data format optimization involves choosing the optimal data format for the input and output tensors of the deep learning model. By using the optimal data format, the amount of data transferred between the CPU and GPU can be minimized, which can improve inference speed.
Compiler Optimizations: Compiler optimizations involve using specialized compilers and programming languages to generate optimized code for the target hardware platform. These compilers can apply a range of optimizations, such as loop unrolling, function inlining, and instruction scheduling, to improve the performance of the deep learning model.

Overall, DNN inference optimizations are critical for achieving high performance and efficiency in deep learning models, particularly when deploying models on edge devices and other resource-constrained platforms.

We can use TFLite EDGE converted model from Tensorflow Keras model.

To convert and use a TensorFlow Lite (TFLite) edge model, you can follow these general steps:

Train your model: First, train your deep learning model on your dataset using TensorFlow or another deep learning framework. Once you have a trained model, you can convert it to the TFLite format for deployment on edge devices.
Convert the model to TFLite format: To convert your model to the TFLite format, you can use the TensorFlow Lite Converter tool. This tool takes a TensorFlow model as input and produces a TFLite model that can be deployed on edge devices. The TFLite Converter supports a wide range of conversion options, including quantization, pruning, and other optimizations that can improve the performance and efficiency of the model.
Test the TFLite model: Once you have converted your model to the TFLite format, you can test it using the TensorFlow Lite Interpreter. The interpreter allows you to load and run the TFLite model on a variety of edge devices, including Android and iOS devices, microcontrollers, and embedded systems.
Deploy the TFLite model: Once you have tested the TFLite model and verified that it is working correctly, you can deploy it on your edge device. The process for deploying the model will depend on the specific device and platform you are using.

In general, using TFLite edge models involves optimizing the model for efficient execution on resource-constrained devices while minimizing the loss in performance. Some common techniques used for this include quantization, pruning, and other optimizations that can reduce the memory and computation requirements of the model. Once the model is optimized, it can be deployed on a wide range of edge devices, from mobile phones to microcontrollers, for a variety of use cases, such as object detection, image classification, and speech recognition.

Some example code

from keras.models import Model
from keras.layers import Dense, Flatten, BatchNormalization


# Add your custom layers on top of the base model
model = Sequential()
model.add(resnet)
model.add(Dense(1024, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

..............................................
..............................................
model.compile(..................)
model.fit(..........................)
.............................................
..............................................
# Save the trained model
model.save('test_model.h5')
.........................................
...............................................
import tensorflow as tf

# Convert the model
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
.......................................................
........................................................
# Save the model.
with open('test_model.tflite', 'wb') as f:
  f.write(tflite_model)
......................................................
....................................................
# A generator that provides a representative dataset
def representative_data_gen():
  dataset_list = tf.data.Dataset.list_files(test_dir + '/*/*')
  for i in range(100):
    image = next(iter(dataset_list))
    # file_type = os.path.splitext(image)[1]
    # if file_type not in ['.jpeg', '.jpg', '.png', '.bmp']:
    #   continue
    try:
      image = tf.io.read_file(image)
      image = tf.io.decode_jpeg(image, channels=3)
      image = tf.image.resize(image, [IMAGE_WIDTH, IMAGE_HEIGHT])
      image = tf.cast(image / 255., tf.float32)
      image = tf.expand_dims(image, 0)
    except tf.errors.InvalidArgumentError as e:
      continue
    yield [image]

converter = tf.lite.TFLiteConverter.from_keras_model(model)
# This enables quantization
converter.optimizations = [tf.lite.Optimize.DEFAULT]
# This sets the representative dataset for quantization
converter.representative_dataset = representative_data_gen
# This ensures that if any ops can't be quantized, the converter throws an error
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
# For full integer quantization, though supported types defaults to int8 only, we explicitly declare it for clarity.
converter.target_spec.supported_types = [tf.int8]
# These set the input and output tensors to uint8 (added in r2.3)
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model = converter.convert()

with open('test_model_edge.tflite', 'wb') as f:
  f.write(tflite_model)
...............................................................
...............................................................
! curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

! echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list

! sudo apt-get update

! sudo apt-get install edgetpu-compiler 
............................................................
.............................................................
! edgetpu_compiler test_model_edge.tflite
..........................................................
..........................................................
print (train_generator.class_indices)

labels = '\n'.join(sorted(train_generator.class_indices.keys()))

with open('test_labels.txt', 'w') as f:
  f.write(labels)
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