What is image classification?

Image classification is the process of categorizing and labeling pixels or groups of pixels in satellite or aerial images based on their spectral values.

This process allows us to analyze and interpret complex patterns and information contained within the images, transforming raw data into more meaningful information that can be used for various applications.

Stay up to date, listen to our geospatial podcast!

Image classification in GIS and remote sensing

Image classification plays a critical role in GIS and remote sensing, as it helps in extracting valuable information from the remotely sensed data.

This information can be utilized for various purposes, such as land use and land cover mapping, urban planning, agriculture monitoring, natural resource management, and environmental studies, among others.

By categorizing the pixels into different classes, image classification simplifies the data and makes it easier for users to analyze and understand spatial patterns, trends, and relationships.

Image Classification Basics

Image classification is the process of categorizing and labeling pixels or groups of pixels in satellite or aerial images based on their spectral values. This process allows us to analyze and interpret complex patterns and information contained within the images, transforming raw data into more meaningful information that can be used for various applications.

Purpose of image classification in GIS and remote sensing

Image classification plays a critical role in GIS and remote sensing, as it helps in extracting valuable information from the remotely sensed data. This information can be utilized for various purposes, such as land use and land cover mapping, urban planning, agriculture monitoring, natural resource management, and environmental studies, among others. By categorizing the pixels into different classes, image classification simplifies the data and makes it easier for users to analyze and understand spatial patterns, trends, and relationships.

Types of image classification

There are two primary types of image classification methods used in GIS and remote sensing:

Supervised image classification:

This method relies on the user’s knowledge and expertise to provide a set of training samples for different classes of interest.

The classifier algorithms then learn from these training samples and apply this knowledge to classify the entire image into the desired categories. This method usually results in higher accuracy, as it incorporates the user’s domain knowledge, but requires more time and effort to collect the training samples.

Unsupervised image classification:

In this method, the classification process is carried out without any prior information or training samples. Instead, the classifier algorithms group the pixels into different clusters based on their spectral values and natural similarities.

The user then assigns meaningful labels to these clusters based on their understanding of the study area. Unsupervised classification is less time-consuming but may not achieve the same level of accuracy as supervised classification, as it doesn’t incorporate any expert knowledge.

Supervised Image Classification

Supervised image classification is a method where the user provides a set of labeled training samples for each class of interest. The classifier algorithms use these training samples to learn the characteristics of each class and then apply this knowledge to classify the entire image into the specified categories. This method relies on the user’s expertise and understanding of the study area and typically results in higher accuracy compared to unsupervised classification.

Key components

1. Training samples: These are representative examples of each class of interest, selected by the user based on their knowledge of the study area. The quality and quantity of training samples directly affect the classification accuracy.
2. Classifier algorithms: These are machine learning algorithms that learn from the provided training samples and generalize this knowledge to classify the entire image.

Steps involved in supervised image classification

1. Data preprocessing: This step involves correcting any distortions or errors in the image data, such as atmospheric, radiometric, or geometric corrections.
2. Selection of training samples: The user selects representative samples for each class of interest based on their knowledge of the study area and the image data.
3. Feature extraction: This step involves extracting relevant features, such as spectral, textural, or contextual information, from the image data to improve classification performance.
4. Training the classifier: The selected classifier algorithm learns the characteristics of each class from the provided training samples.
5. Classification: The trained classifier algorithm is applied to the entire image, categorizing each pixel into one of the specified classes.
6. Accuracy assessment and refinement: The classification results are evaluated for accuracy, often using ground truth data or expert knowledge. If necessary, the classification process may be iteratively refined by adjusting training samples or classifier parameters.

Common supervised classification algorithms

1. Maximum likelihood classifier: A statistical-based algorithm that assumes each class follows a Gaussian distribution and assigns pixels to the class with the highest likelihood based on their spectral values.
2. Support vector machines: A machine learning algorithm that finds the optimal hyperplane to separate different classes by maximizing the margin between them.
3. Decision trees: A hierarchical approach that splits the data into subsets based on specific rules or conditions at each node of the tree until the pixel is assigned to a class.

Advantages and disadvantages of supervised classification

Advantages:

• Generally higher accuracy compared to unsupervised classification, as it incorporates the user’s domain knowledge.
• Allows for more control over the classification process, as users can specify the classes of interest and adjust the training samples.

Disadvantages:

• Requires more time and effort to collect representative training samples.
• Can be prone to overfitting if the training samples do not adequately represent the variability within each class.

Unsupervised Image Classification

Unsupervised image classification is a method where the classification process is carried out without any prior information or training samples. Instead, the clustering algorithms group the pixels into different clusters based on their spectral values and natural similarities.

The user then assigns meaningful labels to these clusters based on their understanding of the study area. This method is less time-consuming but may not achieve the same level of accuracy as supervised classification, as it doesn’t incorporate any expert knowledge.

Key components

1. Clustering algorithms: These are unsupervised machine-learning algorithms that group similar pixels into clusters based on their spectral values without any prior information.
2. Number of clusters: The user must specify the desired number of clusters for the classification process, which can impact the classification results and interpretation.

Steps involved in unsupervised image classification

1. Data preprocessing: As with supervised classification, this step involves correcting any distortions or errors in the image data, such as atmospheric, radiometric, or geometric corrections.
2. Feature extraction: Relevant features, such as spectral, textural, or contextual information, are extracted from the image data to improve the clustering process.
3. Cluster analysis: The selected clustering algorithm is applied to the preprocessed image data, grouping pixels into the specified number of clusters based on their spectral values and similarities.
4. Labeling of clusters: The user assigns meaningful labels to the resulting clusters based on their understanding of the study area and the image data.
5. Accuracy assessment and refinement: The classification results are evaluated for accuracy, often using ground truth data or expert knowledge. If necessary, the classification process may be iteratively refined by adjusting the number of clusters or clustering algorithm parameters.

Common unsupervised classification algorithms

1. K-means: A popular clustering algorithm that aims to minimize the within-cluster sum of squares by iteratively updating the cluster centroids and assigning pixels to the closest centroid.
2. ISODATA (Iterative Self-Organizing Data Analysis Technique): An iterative clustering method that allows for cluster merging and splitting based on user-defined parameters, making it more flexible than K-means.
3. Hierarchical clustering: A clustering method that builds a tree-like structure of nested clusters based on a similarity metric, which can be cut at a specific level to obtain the desired number of clusters.

Advantages and disadvantages of unsupervised classification

Advantages:

• Less time-consuming, as it does not require the collection of training samples.
• Can discover unknown or unexpected patterns in the image data, as it does not rely on prior knowledge.

Disadvantages:

• Generally lower accuracy compared to supervised classification, as it does not incorporate any expert knowledge.
• The resulting clusters may not have clear or meaningful boundaries, making it difficult for users to assign accurate labels.
• Requires the user to determine the appropriate number of clusters, which can be challenging and may impact classification results.

This table describes the differences between supervised and unsupervised image classification

Choosing Between Supervised and Unsupervised Image Classification

Factors to consider

1. Data availability: If you have access to reliable and representative ground truth data or training samples, supervised classification is likely to be more suitable. However, if such data is unavailable or difficult to obtain, unsupervised classification may be a better option.
2. Expertise and time constraints: Supervised classification requires more time and effort to collect and label training samples, as well as a deeper understanding of the study area. If you have limited time or expertise, unsupervised classification may be more suitable.
3. Complexity of the study area: If the study area is complex with a large number of classes or highly variable within-class characteristics, supervised classification might be more appropriate due to its ability to incorporate expert knowledge. On the other hand, unsupervised classification can be useful for exploring unexpected patterns or when the study area is relatively simple and well-defined.
4. Desired accuracy: Generally, supervised classification offers higher accuracy due to the use of training samples. If a high degree of accuracy is critical for your project, supervised classification may be the better choice. However, if the focus is on identifying patterns or trends rather than precise classification, unsupervised classification might be sufficient.

Hybrid approaches: combining supervised and unsupervised classification

In some cases, it might be beneficial to combine both supervised and unsupervised classification methods. This hybrid approach can leverage the strengths of each method while mitigating their weaknesses.

For instance, unsupervised classification can be used initially to explore the data and identify patterns or potential classes. The resulting clusters can then serve as a starting point for collecting representative training samples, which can be used in supervised classification for more accurate and meaningful results. This approach can save time and effort while still achieving a high level of accuracy and interoperability.

What is overfitting in image classification

In the context of image classification, overfitting refers to a situation where a classifier algorithm learns to fit the training data too closely, capturing noise and specific details of the training samples instead of generalizing the underlying patterns. As a result, the classifier may perform very well on the training samples but poorly on new, unseen data.

Overfitting can occur in supervised image classification when the training samples do not adequately represent the variability within each class or when the classifier model is too complex. In these cases, the classifier may learn to recognize the specific characteristics of the training samples rather than the general features of the class, leading to a decrease in the classification accuracy when applied to the entire image or other unseen data.

To mitigate overfitting, it is essential to ensure that the training samples are representative of the different classes and to consider using simpler classifier models or regularization techniques that encourage the model to focus on the most relevant features.

Conclusion

Supervised image classification relies on the user’s expertise and knowledge to provide a set of training samples for different classes of interest. The classifier algorithms learn from these samples and classify the entire image based on this knowledge. In contrast, unsupervised image classification does not use any prior information or training samples. Instead, clustering algorithms group pixels into different clusters based on their spectral values and natural similarities.

Choosing the right image classification method for a specific project is crucial, as it can significantly impact the accuracy and interpretability of the results. Factors such as data availability, expertise and time constraints, complexity of the study area, and desired accuracy should be considered when deciding between supervised and unsupervised classification methods.

While each image classification method has its strengths and weaknesses, it is essential to explore and experiment with both supervised and unsupervised methods to gain a comprehensive understanding of their capabilities and limitations.

This hands-on experience will help you to master the GIS and remote sensing techniques, allowing you to make informed decisions and select the most appropriate method for your specific project requirements. Moreover, combining both methods in a hybrid approach can yield valuable insights and improved classification results by leveraging the advantages of each method.