PySpark Tutorial

PySpark is an API of Apache Spark which is an open-source, distributed processing system used for big data processing which was originally developed in Scala programming language at UC Berkely. The Spark has development APIs in Scala, Java, Python, and R, and supports code reuse across multiple workloads — batch processing, interactive queries, real-time analytics, machine learning, and graph processing. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. …

Machine Learning | Remote Sensing

This article helps readers to better understand land cover classification on Sundarbans satellite data using different classification algorithms with Python.

Table of Contents

1. Sundarbans Satellite Imagery
2. K-Nearest Neighbor Classifier (K-NNC)
3. Support Vector Machine (SVM)
4. Gradient Boosting Classifier
5. Conclusion
6. References

Let’s Get Started…

Sundarbans Satellite Imagery

The Sundarbans is one of the largest mangrove areas in the delta formed by the confluence of the Ganges, Brahmaputra, and Meghna rivers in the Bay of Bengal. The Sundarbans forest is about 10,000 sq km across India and Bangladesh, of which 40% lies in India, and is home to many rare and globally threatened wildlife species. The…

Hands-on Tutorials, Data science | Remote sensing | Hands-on Tutorial

This article helps users to understand the different methods and supervised and unsupervised machine learning techniques to analyze satellite imagery using Python with hands-on tutorials and examples.

Contents

1. Satellite Imagery Indices
2. Supervised Learning in Satellite Imagery
3. Unsupervised Learning in Satellite Imagery
4. Conclusion

Let’s get started ✨

Satellite Imagery Indices

Normalized Satellite indices are images that are computed from Multi-Spectral satellite images. These images emphasize a specific phenomenon that is present while mitigating other factors that degrade the effects in the image. For instance, a vegetation index will show healthy vegetation as bright in the index image, while unhealthy vegetation has lower values and barren…

Data Science | Data Visualization

There is no such thing as information overload. There is only a bad design. — Edward Tufte

We all know a picture is worth a thousand words, data visualization is the visual summary of the information that makes it easier to understand/identify patterns and trends instead of looking at thousands of rows in spreadsheets. A good data visualization place the meaning of complex datasets in a precise and concise way. An interactive data visualization makes it even easier to understand and find insights from the data.

This article covers creating different interactive plots using Plotly and embedding interactive data visualizations…

Remote Sensing | Data Analysis | Python

This article helps readers to better understand the satellite data and different methods to explore and analyze the Sundarbans satellite data using Python.

Table of Contents

1. Sundarbans Satellite Imagery
2. Data Analysis
3. Vegetation and Soil Indices
4. Water Indices
5. Geology Indices
6. Conclusion
7. References

Let’s get started ✨

Sundarbans Satellite Imagery

The Sundarbans is one of the largest mangrove areas in the delta formed by the confluence of the Ganges, Brahmaputra, and Meghna rivers in the Bay of Bengal. The Sundarbans forest is about 10,000 sq km across India and Bangladesh, of which 40% lies in India, and is home to many rare and globally threatened wildlife species. …

Deep Learning | Remote Sensing

Land cover classification using remote sensing data is the task of classifying pixels or objects whose spectral characteristics are similar and allocating them to the designated classification classes, such as forests, grasslands, wetlands, barren lands, cultivated lands, and built-up areas. Various techniques have been applied to land cover classification, including traditional statistical algorithms and recent machine learning approaches, such as random forest and support vector machines, e.t.c.

This article covers a hands-on Python tutorial on the land cover classification of satellite imagery using Convolutional Neural Network (CNN).

Table of Contents

1. Introduction
2. Convolutional Neural Networks (CNN)
3. Salinas HSI
4. Implementation of CNN
5. Training
6. Results
7. Conclusion
8. …

Deep Learning | Python HandsOn

Dimensionality reduction has become an important aspect of machine learning. It is often considered as a preprocessing step in the machine learning problems such as classification and clustering. The presence of a large number of features in data sets affects the predictive capabilities of the classifiers. The feature extraction algorithms reduce the dimensionality of the data set, thereby paving way for the classifiers to generate comprehensive models at a reduced computational cost.

This article helps readers to understand the role of AutoEncoders in Dimensionality Reduction of Hyperspectral Images and also provides a hands-on tutorial of the implementation.

Table of Contents

1. Introduction
2. AutoEncoders
3. Implementation…

Hands-on Tutorials, Deep Learning

Table of Contents

1. Introduction to Hyperspectral Images (HSI)
2. Pavia University HSI
3. Deep Neural Networks
4. Implementation of Deep Neural Networks
5. Training Deep Neural Network
6. Results
7. Conclusion
8. References

Introduction to Hyperspectral Images (HSI)

Hyperspectral Imaging is an important technique in remote sensing, which collects the electromagnetic spectrum ranging from the visible to the near-infrared wavelength. Hyperspectral imaging sensors often provide hundreds of narrow spectral bands from the same area on the surface of the earth. In hyperspectral images (HSI), each pixel can be regarded as a high-dimensional vector whose entries correspond to the spectral reflectance in a specific wavelength.

With the advantage of distinguishing subtle spectral differences, HSIs have been…