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Understanding principal Component Analysis In Earth Observation

What is Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. 

The transformation is defined in such a way that:

1. The first principal component has the largest possible variance (that is, it accounts for as much of the variability in the data as possible),

2. Each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to (i.e., uncorrelated with) the preceding components.

PCA is used extensively in exploratory data analysis and for making predictive models. It is a popular method for feature extraction and data reduction by creating new uncorrelated variables that successively maximize variance.

In the context of remote sensing, PCA is used to reduce the amount of redundant information that is often present in the data. Especially for hyperspectral data, where there might be hundreds or even thousands of spectral bands with highly correlated information, PCA can be very useful. It transforms the data into a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. This way, it helps to summarize the key information in a manageable format, reducing the dimensionality and complexity of the data.

How does PCA help in reducing the dimensionality of the Earth observation data?

Principal Component Analysis (PCA) is a method used to reduce the dimensionality of large datasets while preserving as much information as possible. 

In the context of Earth observation data, which often involves collecting information across a broad spectrum (and therefore, many variables), PCA can be particularly useful. Here’s how it works:

Correlation Identification

PCA identifies correlated variables in the dataset. For instance, in remote sensing, many of the spectral bands in hyperspectral images are often highly correlated because they record similar information.


It then performs a mathematical transformation of the original data to a new coordinate system. The new axes, or “principal components”, that it creates are orthogonal (i.e., uncorrelated), and each component is a linear combination of the original variables. 

Variance Maximization

The first principal component is chosen in such a way that it accounts for the maximum variance in the dataset. Each subsequent component is orthogonal to the preceding ones and accounts for the maximum remaining variance.

Dimensionality Reduction

The beauty of PCA is that it ranks the principal components by the amount of original variance they account for. So, a small number of principal components can capture most of the variability in the original data. This means we can reduce the dimensionality of the data by choosing to keep only the first few principal components and ignore the rest, which contain only a small fraction of the original information.

The result is a much simpler dataset that still retains most of the meaningful information from the original data, which makes subsequent analyses more manageable and less computationally intensive. This is particularly useful in earth observation applications, where datasets can be very large and complex.

How does PCA affect the accuracy of the models or algorithms used in Earth observation? Does it improve the performance of these models?

The effect of Principal Component Analysis (PCA) on the accuracy of models or algorithms used in Earth observation depends on the specific context, including the quality and complexity of the original data, the nature of the analysis or model, and the precise way PCA is implemented.

PCA can indeed improve the performance of these models in certain circumstances:

Reduced Overfitting

By reducing the dimensionality of the data, PCA can help prevent overfitting, which is a common problem in machine learning when a model is too complex and performs well on training data but poorly on new, unseen data. With fewer, more meaningful features, the model is less likely to ‘learn’ noise in the training data and more likely to generalize well to new data.

Improved Computational Efficiency

PCA can make algorithms run faster and more efficiently, which can be especially valuable when dealing with large Earth observation datasets. Reduced computation time can indirectly lead to better models as it allows for more extensive parameter tuning or more complex modeling approaches that wouldn’t be feasible with the full dataset.

Mitigation of Multicollinearity

Multicollinearity (where predictor variables in a regression model are highly correlated) can inflate the variance of the regression coefficients and make the model unstable and difficult to interpret. Since PCA creates new uncorrelated variables, it can help mitigate the issues caused by multicollinearity.

However, it’s important to note that PCA also has potential drawbacks:

Loss of Interpretability

The principal components are linear combinations of the original variables and often do not have a clear, straightforward interpretation in terms of the original data. This can make models based on PCA-transformed data more difficult to interpret.

Possible Information Loss

While PCA aims to retain as much of the variance in the data as possible, some information is inevitably lost when reducing the dimensionality of the data. If the discarded components (those with smaller eigenvalues) contain information that is relevant to the prediction task, this could potentially reduce the accuracy of the resulting models.

How can PCA help to differentiate between important and less important variables in a dataset?

Principal Component Analysis (PCA) works by transforming the original variables of a dataset into a new set of uncorrelated variables, known as principal components. These new variables are formed as linear combinations of the original ones, and they are ordered so that the first few retain most of the variation present in all of the original variables.

Here’s how PCA can help to differentiate between important and less important variables:

Variance Contribution

Each principal component contributes a certain amount of variance (information) to the dataset. The first principal component accounts for the largest possible variance, the second principal component (orthogonal to the first) accounts for the second largest variance, and so on. The importance of a variable can be gauged by seeing how much it contributes to these components with large variances.

Component Loadings

The coefficients in the linear combinations that define the principal components, also known as loadings, reflect the correlation between the original variables and the principal component. A larger absolute value of the loading indicates that the variable is more important in calculating the component, thus suggesting that it’s an important feature in the dataset.

However, a few points to remember:

  • PCA assumes that the importance of a variable is based on how much variance it explains in the dataset. In certain scenarios, a variable that explains less variance can still be crucial depending on the context.
  • After PCA, the newly formed variables (principal components) might not carry the same interpretable meaning as the original variables, which might make the interpretation of importance less straightforward.
  • While PCA identifies important variables based on their contribution to the variance, it does not consider the impact of the variables on a specific dependent variable (in case of a predictive model). For this purpose, other techniques like regression analysis or feature importance in machine learning models might be more suitable.

How does PCA deal with noise in Earth observation data? Does it help to enhance the signal-to-noise ratio?

PCA can be an effective technique for dealing with noise in Earth observation data and enhancing the signal-to-noise ratio. Here’s how it works:

  1. Removing Less Important Information: In PCA, principal components are ranked by the amount of variance they explain in the data. The first few components (i.e., those that explain the most variance) are generally considered to contain the “signal”, or the most valuable information. The later components, which explain only a small amount of variance, often contain mostly noise. By discarding these later components and only keeping the first few, PCA essentially filters out much of the noise from the data.
  2. Data Compression: By reducing the dimensionality of the data, PCA also compresses the data, which can help to reduce noise. Compression works by averaging out and removing some of the random variation (or noise) in the data, which can make the underlying patterns (or signals) clearer.
  3. Uncorrelated Features: PCA transforms the original, potentially correlated variables into a new set of uncorrelated variables. This means that each principal component captures a unique pattern in the data, which can help separate the signal from the noise.

It’s important to note a couple of things

  • If noise is present in all bands and represents a substantial portion of the signal, PCA might not effectively separate the signal from the noise. PCA assumes that the noise is uncorrelated and has a lower variance than the signal, which might not always be the case.
  • Discarding later components might also eliminate some subtle but important signals in the data, leading to a loss of valuable information.

So while PCA can be a useful tool for noise reduction and signal enhancement, it’s not a silver bullet and should be used carefully, in combination with other techniques, and with a good understanding of the data and the specific context.

Applications related to Earth observation where PCA is particularly beneficial

Principal Component Analysis (PCA) is beneficial in various scenarios related to Earth observation data, given its ability to reduce dimensionality, mitigate multicollinearity, and improve computational efficiency. Here are a few specific applications where PCA can be particularly useful:

  1. Multispectral and Hyperspectral Imaging: These imaging techniques generate large datasets with potentially hundreds of correlated spectral bands. PCA can help to reduce the dimensionality of these datasets and eliminate redundancy, making the data more manageable and the analysis more efficient.
  2. Change Detection: PCA can be applied to multitemporal datasets (data collected over different periods) to highlight changes in land use, vegetation, urban growth, etc. The first few principal components usually capture the majority of the variance in the data and can help in identifying significant changes over time.
  3. Noise Reduction: PCA can be useful in situations where Earth observation data is noisy. As explained earlier, it’s possible to discard the later principal components, which tend to capture less signal and more noise, thereby improving the signal-to-noise ratio.
  4. Feature Extraction: In remote sensing, PCA is often used for feature extraction. This is particularly useful in machine learning applications where derived features (principal components) may be used instead of the original data to train models, improving efficiency and potentially model performance.
  5. Data Compression: Large Earth observation datasets, such as those generated by modern satellites, can be unwieldy and challenging to work with due to their size. PCA can be used to compress these datasets, making them more manageable without losing too much valuable information.
  6. Climate Studies: PCA can help identify patterns in climatic data (like temperature, precipitation, etc.) over time and space, and can be used to study large-scale climate phenomena like the El Niño Southern Oscillation.

How does PCA work in multispectral vs. hyperspectral imaging data in terms of reducing dimensionality and enhancing information extraction?

Principal Component Analysis (PCA) is a powerful tool used extensively in both multispectral and hyperspectral remote sensing for dimensionality reduction and information extraction. Here’s how it works in each context:

  1. Multispectral Imaging: In multispectral imaging, there are fewer bands, typically ranging from 3 to 10. These bands cover broad regions of the electromagnetic spectrum, like the visible light spectrum and near-infrared. While the correlation among bands in multispectral data is typically lower than in hyperspectral data, PCA can still be beneficial for identifying the primary modes of variability in the data and reducing dimensionality. The first few principal components (PCs) usually capture the majority of the variability in the original data, and they can be used to identify and interpret significant patterns. This helps to enhance information extraction by focusing on the most important aspects of the data and reducing noise and redundancy.
  2. Hyperspectral Imaging: Hyperspectral imaging involves collecting and processing information from across the electromagnetic spectrum, using many more bands (up to a few hundred) than multispectral imaging. The bands in hyperspectral data are narrow and contiguous, and the data is typically highly correlated. This is where PCA is particularly beneficial. PCA transforms the hyperspectral data into a set of new variables (PCs) that are uncorrelated and ordered by the amount of variance they explain. Typically, only a few PCs are needed to capture most of the variance in the hyperspectral data, substantially reducing its dimensionality. The PCs can also highlight important patterns in the data that were not apparent in the original bands, aiding in information extraction.

It’s important to remember that while PCA can reduce dimensionality and aid in information extraction, it doesn’t always enhance interpretability, as the PCs may not correspond to physically meaningful quantities. Moreover, PCA is a linear method and may not capture non-linear relationships in the data. Depending on the specific application and analysis goals, other techniques may be needed alongside or instead of PCA.

About the Author
I'm Daniel O'Donohue, the voice and creator behind The MapScaping Podcast ( A podcast for the geospatial community ). With a professional background as a geospatial specialist, I've spent years harnessing the power of spatial to unravel the complexities of our world, one layer at a time.