What is the difference between pixel-based and object-based remote sensing image analysis?

1 Pixel-based image analysis

Pixel-based image analysis is the traditional and most common method of remote sensing image classification. It involves assigning a class or category to each pixel in the image based on its spectral value, which represents the reflectance of electromagnetic radiation at different wavelengths. Pixel-based image analysis can use supervised or unsupervised techniques, depending on whether the user provides training samples or not. Pixel-based image analysis is relatively simple, fast, and widely available, but it has some limitations. For example, it can produce noisy and speckled results, it can be affected by atmospheric and environmental conditions, and it can ignore the spatial context and shape of the features in the image.

2 Object-based image analysis

Object-based image analysis is a newer and more advanced method of remote sensing image classification. It involves segmenting the image into homogeneous and meaningful regions or objects, and then assigning a class or category to each object based on its spectral, spatial, and contextual attributes. Object-based image analysis can also use supervised or unsupervised techniques, but it requires more parameters and criteria to define the objects and their characteristics. Object-based image analysis is more complex, time-consuming, and less standardized, but it has some benefits. For example, it can produce more accurate and realistic results, it can handle high-resolution and multispectral images better, and it can capture the semantic and structural information of the features in the image.

3 Comparing pixel-based and object-based image analysis

When choosing between pixel-based and object-based image analysis, there are several factors to consider. The spatial and spectral resolution of the image can influence the choice of method; higher resolution images may benefit from object-based analysis, while lower resolution images may be more suitable for pixel-based analysis. The type and complexity of the features should also be taken into account; features with distinct and uniform spectral signatures can be classified by pixel-based image analysis, whereas features with complex shapes and textures may be better represented by object-based image analysis. Additionally, the availability and quality of training data should be considered; pixel-based image analysis may require less training data but they must be representative and accurate, whereas object-based image analysis may need more data but can be more flexible and adaptable. Lastly, the computational and analytical resources should be taken into account; pixel-based image analysis can generally be performed by many software tools and is simpler to implement, while object-based image analysis may require more specialized software tools and can be more demanding to execute.

4 Examples of pixel-based and object-based image analysis

To illustrate the difference between pixel-based and object-based image analysis, let's consider their applications to various types of images and features. Land cover and land use classification is one of the most common uses of remote sensing image analysis, and it involves identifying and mapping the types and functions of the land surface. Pixel-based image analysis is typically effective for large and homogeneous land cover types, such as water or forest, while object-based image analysis is better suited for small and heterogeneous land use types like urban areas. Change detection is another important application of remote sensing image analysis, which involves detecting and measuring changes in the land surface over time. Pixel-based image analysis is suitable for detecting changes in spectral values, while object-based image analysis is advantageous for detecting changes in spatial patterns. Finally, object recognition is a more specific and advanced application of remote sensing image analysis, which involves identifying and locating individual objects or features in the image. Pixel-based image analysis can be limited for recognizing objects with similar or variable spectral values, while object-based image analysis can be powerful for recognizing objects with distinct or consistent spatial attributes.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?