Automated feature extraction has long been considered the Holy Grail of remote sensing. One of your esteemed instructors has even written an article on this subject: Automated Feature Extraction: The Quest for the Holy Grail, which you should, of course, read. The appeal of simply pushing a button and having all the features of interest in an image identified is understandably appealing. That being said, automated feature extraction requires expensive technology and highly trained people. The task should only be undertaken if the economies of scale are present. For example, developing an algorithm to count planes from one satellite image taken of an airport would be a waste of time; it would be quicker, more accurate, and more cost effective to do it manually. An algorithm that could count planes from a satellite image of any airport in the world would be very valuable, as such a task would be extremely costly, requiring a large number of human analysts.
Automated feature extraction typically requires high-quality data. Changes in an image such as shifts in the tone or the direction of shadows may have little to no effect on a human analyst but can wreak havoc on an automated approach. The success of an automated workflow often hinges on the quality of the remotely sensed data used, along with the methods employed to preprocess the data. Recent work points to the advantage gained from integrating multiple types of remotely sensed data into the feature extraction process, particularly if the modalities complement each other (i.e., imagery and lidar). One of the points made in the article: Automated Feature Extraction: The Quest for the Holy Grail is that one of the great advantages that the human analyst has is that he/she can perceive depth from 2D imagery due to the presence of shadows. Lidar, when integrated with imagery into an automated workflow, more closely approximates the unique recognition abilities of the human analyst.
It would be impossible to cover all the approaches to automated feature extraction, as the field is changing rapidly. One of the most recent advances has been the move away from pixel-based approaches to object-based approaches, particularly when it comes to extracting features from high-resolution data. In keeping with the times, this lesson will have a focus on the application of object-based methods to automated feature extraction tasks, making full use of the elements of image interpretation you studied in the previous lesson.
In this lesson, you will learn the techniques, tools, and procedures used to automatically extract information from remotely sensed data. Although “nothing beats a human,” the reality is that due to the vast amounts of remotely sensed data being acquired and the relatively slow (and thus costly) rate at which human interpreters work, automation is necessary if we are to turn these data into actionable information. In the civilian remote sensing community, the common term for such automated approaches is "classification." "Image classification" has an entirely different meaning in the defense community, thus the term "terrain categorization" (TERCAT) was adopted. Given the confusion surrounding the term "classification" and the fact that "terrain categorization" does not adequately describe all that this segment of remote sensing comprises, we will use the term "feature extraction" instead.
At the end of this lesson, you will be able to:
- discuss pixel and object-based approaches to feature extraction;
- discuss supervised and unsupervised approaches to feature extraction;
- carry out a classification using spectral information;
- carry out a classification using geometric information;
- carry out a classification using texture information;
- carry out a classification using contextual information;
- analyze multispectral imagery and lidar using object-based techniques;
- create an object-based workflow for extracting information from multiple types of geospatial data.
If you have any questions now or at any point during this week, please feel free to post them to the Lesson 4 Questions and Comments Discussion Forum in Canvas.