Remote sensing is a powerful tool for mapping and monitoring the Earth’s surface. It involves the acquisition and analysis of data from various sensors, such as satellites, aircraft, and drones. One of the key applications of remote sensing is land cover classification, which involves categorizing different land cover types based on their spectral properties.
There are two main approaches to land cover classification: supervised and unsupervised classification. In this article, we will discuss the difference between supervised and unsupervised classification in remote sensing.
Supervised classification is generally more accurate than unsupervised classification because the user has control over the classification process and can ensure that the training data is representative of the land cover types.
What is Supervised Classification In Remote Sensing?
Supervised classification is a technique in remote sensing where a set of training data is used to classify pixels in an image. The training data consists of samples of each land cover type that the user wants to map. The user manually selects the training samples from the image and assigns them to their respective land cover classes. The classifier then uses this training data to classify the remaining pixels in the image.
Supervised classification is a more accurate approach than unsupervised classification because the user has control over the classification process. However, it requires more effort and expertise to select the training samples and assign them to the correct classes.
The main advantage of Supervised Classification is that it allows for accurate and efficient classification of large areas. It is also a flexible technique that can be used for a variety of applications.
Training Data
The selection of training data is a crucial step in supervised classification. It is essential to select representative pixels for each class that capture the spectral variability of that class. The number of training pixels needed for accurate classification depends on the complexity of the study area and the number of classes.
Algorithm
Several algorithms can be used for supervised classification, including Maximum Likelihood, Support Vector Machines (SVM), and Decision Trees. The Maximum Likelihood algorithm is a statistical approach that assumes the distribution of pixel values in each class follows a normal distribution.
The SVM algorithm is a machine learning technique that finds the optimal hyperplane to separate the classes. The Decision Trees algorithm is a hierarchical approach that divides the data into smaller subsets based on the spectral characteristics of the pixels.
Accuracy Assessment
The accuracy of the classification depends on the quality of the training data and the choice of the algorithm. To assess the accuracy of the classification, a set of validation data is used, which are pixels that are not used in the training phase. The user compares the classified pixels with the validation data and calculates the overall accuracy and the class-specific accuracy.
What is Unsupervised Classification In Remote Sensing?
Unsupervised classification is a technique in remote sensing where the classification algorithm automatically groups pixels with similar spectral properties into clusters. The user does not need to provide any training data to the algorithm. Instead, the algorithm analyzes the spectral properties of the image and identifies clusters of pixels with similar spectral properties.
Unsupervised classification is a faster and easier approach than supervised classification because the user does not need to provide any training data. However, it is less accurate than supervised classification because the algorithm does not have any information about the land cover types.
Clustering
The unsupervised classification algorithm uses clustering techniques to group pixels with similar spectral properties. The clustering algorithm assigns each pixel to a cluster based on its spectral similarity to the other pixels in that cluster. The user then assigns land-cover classes to each cluster based on their knowledge of the study area.
Number of Classes
The user must determine the number of classes after the unsupervised classification algorithm has grouped the pixels. However, determining the optimal number of classes can be challenging as it depends on the user’s knowledge of the study area and the objectives of the study.
Accuracy Assessment
The accuracy of unsupervised classification is evaluated using the same method as supervised classification. However, the user must assign land-cover classes to each cluster manually, which can be time-consuming and subjective.
READ MORE: Advantages and Disadvantages of Remote Sensing
Differences Between Supervised and Unsupervised Classification
Here are the main differences between supervised and unsupervised classification:
1. Training Data
Supervised classification requires the user to provide a set of training data, while unsupervised classification does not require any training data.
2. User Control
Supervised classification gives the user more control over the classification process because they manually select the training data and assign them to the correct classes. Unsupervised classification is automated and does not require any user input.
3. Accuracy
Supervised classification is more accurate than unsupervised classification because the user has control over the classification process and can ensure that the training data is representative of the land cover types. Unsupervised classification is less accurate because the algorithm does not have any information about the land cover types.
4. Complexity
Supervised classification is more complex and requires more effort and expertise than unsupervised classification because the user needs to select the training data and assign them to the correct classes. Unsupervised classification is simpler and requires less effort because the algorithm automatically groups pixels with similar spectral properties.
Comparison of Supervised and Unsupervised Classification In Remote Sensing (Supervised Vs. Unsupervised)
This table provides a quick overview of the key differences between supervised and unsupervised classification in remote sensing. It highlights the main features of each method, such as the use of training data, the selection of the algorithm, the determination of the number of classes, and the accuracy assessment.
Features | Supervised Classification | Unsupervised Classification |
Training data | User provides training data | No training data required |
Algorithm | User selects the algorithm | Algorithm identifies natural groupings |
Accuracy assessment | User compares classified pixels with validation data | User assigns land-cover classes to clusters manually |
Number of classes | User specifies the number of classes | Algorithm determines the number of classes |
Appropriate for | Specific land-cover classes | Exploratory studies |
Accuracy | More accurate if training data is representative and algorithm is chosen correctly | Less accurate than supervised classification |
Challenges | Dependent on quality of training data and algorithm | Dependent on user interpretation and determination of number of classes |
Applications | Land-use and land-cover mapping, environmental monitoring, natural resources management, disaster management, urban planning, agriculture and forestry | Same as supervised classification |
Applications of Supervised and Unsupervised Classification
Supervised and unsupervised classification have various applications in remote sensing, including:
- Land-use and land-cover mapping
- Environmental monitoring
- Natural resources management
- Disaster management
- Urban planning
- Agriculture and forestry
Which Approach to Use: Supervised Vs Unsupervised?
The choice between supervised and unsupervised classification depends on the specific application and the user’s goals. Supervised classification is more accurate and reliable, especially for complex land cover types that are difficult to distinguish spectrally. However, it requires more effort and expertise to select the training samples and assign them to the correct classes.
Unsupervised classification is faster and easier, but it may not be suitable for all applications. It is best suited for simple land cover types that can be easily distinguished spectrally. In some cases, a combination of supervised and unsupervised classification may be used to achieve better results.
Conclusion
In summary, both supervised and unsupervised classification are important techniques in remote sensing. The choice between the two approaches depends on the specific application and the user’s goals. Supervised classification is more accurate and reliable, but it requires more effort and expertise. Unsupervised classification is faster and easier, but it may not be suitable for all applications. It is best suited for simple land cover types that can be easily distinguished spectrally. A combination of both approaches can also be used to achieve better results.
Overall, it is important to understand the differences between supervised and unsupervised classification in remote sensing to choose the appropriate approach for a given application.
FAQs: Difference Between Supervised and Unsupervised Classification In Remote Sensing
What is land cover classification?
Land cover classification is the process of categorizing different land cover types based on their spectral properties using remote sensing data.
What is supervised classification?
Supervised classification is a technique in remote sensing where a set of training data is used to classify pixels in an image.
What is unsupervised classification?
Unsupervised classification is a technique in remote sensing where the classification algorithm automatically groups pixels with similar spectral properties into clusters.
What are the challenges in classification in remote sensing?
The challenges in classification in remote sensing include atmospheric interference, mixed pixels, spectral variability, seasonal changes, data quality, and the choice of algorithm.