How do you choose between supervised and unsupervised learning for remote sensing?

The choice between supervised and unsupervised learning in remote sensing depends on your specific project requirements, data availability, and resources. Supervised classification is generally more accurate and reliable but requires more effort and expertise. Unsupervised classification is faster and easier but may not be suitable for all applications. Consider your project goals, data quality, and available resources when making this decision.

Choosing between supervised and unsupervised learning for remote sensing depends on the specific objectives of the analysis, the availability of labeled training data, and the nature of the data being analyzed. Supervised learning is preferred when specific classes or features are known and labeled training data is available, while unsupervised learning is more suitable for exploratory analysis or when labeled data is scarce or unavailable. Additionally, hybrid approaches that combine both supervised and unsupervised techniques can be beneficial in certain situations, such as semi-supervised learning or transfer learning, where labeled data is limited but some prior knowledge is available.

In supervised learning, the targets/classes are predefined. Training samples are created from satellite, aerial imagery, or other sources to represent the classes, which are then used by the classifier/algorithm to classify the image - input data. Supervised classifiers include Decision Trees (DT), Random Forests (RF), Support vector machines (SVM), and others. The results obtained in supervised classification depend on the quality and size of training samples, feature/band selection, model hyperparameter tuning, and other factors.

El aprendizaje supervisado es muy útil cuando trabajas con imágenes satelitales y tienes un objetivo claro, como clasificar tipos de vegetación o uso del suelo. Si cuentas con datos bien etiquetados, este enfoque puede ofrecer resultados muy precisos. Sin embargo, la etiquetación de imágenes satelitales es un proceso costoso y largo, ya que requiere un análisis cuidadoso de cada píxel. Además, el modelo puede rendir bien en los datos de entrenamiento, pero fallar con imágenes nuevas debido al sobreajuste. Es esencial considerar si el esfuerzo de etiquetar vale la precisión obtenida.

Supervised learning trains a model using labeled data, where each input has a corresponding output. It's ideal for projects with clear objectives like mapping or detection. While it yields accurate results, manual labeling is time-consuming and costly. Overfitting, where the model fits too closely to the training data, can also be an issue, impacting performance on new data. Despite these challenges, supervised learning remains a powerful tool for tasks with well-defined goals.

Unsupervised learning involves grouping image pixels into various (computer-defined) classes based on the image's inherent pattern and structure. It is suitable for investigating patterns in images, especially when there is not enough knowledge about the classes. Common unsupervised algorithms are K-means Clustering and ISODATA.

The simple and most easy way to choose between them, is based on the available data, and the end use case. For instance, if a researcher has a study area in which he wants to just classify water area from other, then a simple supervised clustering algorithm will work fine. In contrast, if a user wants to classify land into pre-defined classes, he needs to provide a set of training instructions for the model to be able to understand the task. This falls under supervised classification task!

Unsupervised is suitable for investigating patterns in images, especially when there is not enough knowledge about the classes. Supervised classification is more appropriate when the targets/features are known. Training samples are created from satellite, aerial imagery, or other available sources to represent the classes, which are then used by the classifier/algorithm to classify the image.

For remote sensing with SAR data, choosing between supervised, unsupervised, and semi-supervised learning depends on task and data availability. Supervised learning suits when labeled data is abundant, like land cover classification. Unsupervised learning, such as clustering SAR backscatter values, works when there's no labeled data. Semi-supervised learning helps with limited labeled data, as in SAR image segmentation. For glacier monitoring with SAR, supervised learning could classify glacier extent changes, unsupervised learning could identify movement patterns, and semi-supervised learning could enhance accuracy with few labeled images.

Both Supervised and Unsupervised Classifications methods are used for different purposes. Suppose, we have a specific application of interest, e.g. Land Cover Segmentation, in that case we need to use a Supervised Algorithm, in which as the name suggests, we will Supervise the Algorithm to learn and predict different Land Cover Classes. On the other side, suppose we want to Train a Building Footprint Extraction model, but do not have the Ground Truth Labels of the Buildings. In that case we can use an Unsupervised Algorithm e.g. Segment Anything Model (SAM), which will Segment everything in a given Satellite Imagery. Further, we will only select and extract the Features of Interest which are Buildings, which we can use as our Ground Truth.

When choosing between supervised and unsupervised learning for remote sensing, consider the availability of labeled data, the clarity of your analysis objectives, and the complexity of the patterns you want to identify. In many cases, a combination of both methods can be the most effective approach for exploring and extracting useful information from your remote sensing data.

Choosing between supervised and unsupervised learning for remote sensing depends on the user's prior knowledge and project goals. -Supervised learning excels when we have labeled training data for the specific categories we want to identify. It offers higher accuracy but requires upfront effort to create training data. -Unsupervised learning works well for exploration and identifying unknown patterns. It automatically groups pixels based on spectral similarities, but interpreting these clusters into meaningful classes requires additional analysis. A quick guideline: -Known classes & high accuracy needed: Supervised learning -Exploration & unknown patterns: Unsupervised learning We even combine both approaches for a more robust workflow.