Digital image classification uses the quantitative spectral information contained in an image, which is related to the composition or condition of the target surface. Image analysis can be performed on multispectral as well as hyperspectral imagery. It requires an understanding of the way materials and objects of interest on the earth's surface absorb, reflect, and emit radiation in the visible, near-infrared, and thermal portions of the electromagnetic spectrum. In order to make use of image analysis results in a GIS environment, source image should be orthorectified so that the final image analysis product, whatever its format, can be overlaid with other imagery, terrain data, and other geographic data layers. Classification results are initially in raster format, but they may be generalized to polygons with further processing. There are several core principles of image analysis that pertain specifically to the extraction of information and features from remotely sensed data.
- Spectral differentiation is based on the principle that objects of different composition or condition appear as different colors in a multispectral or hyperspectral image. For example, a newly planted cornfield has a distinct color when compared to a field of mature plants, and yet another color when the field has been harvested. Corn has a distinct color as compared to wheat; healthy plants are a different color than pest-infested or drought-impacted plants. The use of spectral signature, or color, to distinguish types of ground cover or objects is called spectral differentiation.
- Radiometric differentiation is the detection of differences in brightness, which may in certain cases be used to inform the image analyst as to the nature or condition of the remotely sensed object.
- Spatial differentiation is related to the concept of spatial resolution. We may be able to analyze the spectral content of a particular pixel or group of pixels in a digital image when those pixels comprise a single homogeneous material or object. It is also important to understand the potential for mixing of the spectral signatures of multiple objects into the recorded spectral values for a single pixel. When designing an image analysis task, it is important to consider the size of the objects to be discovered or studied compared to the ground sample distance of the sensor.
The extraction of information from remotely sensed data is frequently accomplished using statistical pattern recognition; land-use/land-cover classification is one of the most frequently used analysis methods (Jensen, 2005). Land cover refers to the physical material present on the earth’s surface; land use refers to the type of development and activities people undertake in a particular location. The designation of “woodland” for a tree-covered area is a land cover classification; the same woodland might be designated as “recreation area” in a land use classification.
While certain aspects of digital image classification are completely automated, a human image analyst must provide significant input. There are two basic approaches to classification, supervised and unsupervised, and the type and amount of human interaction differs depending on the approach chosen.
- Supervised classification requires the image analyst to choose an appropriate classification scheme, and then identifies training sites in the imagery that best represent each class. A simple land cover classification scheme might consist of a small number of classes, such as urban, water, wetlands, forest, grass/crops. Individual sites that fall into a single class may have slightly different spectral characteristics; for example, the spectral signature of a water body will depend on the amount of suspended sediment or plant material in the water. Urban land cover signatures will vary based on the type of materials used; asphalt has a very different spectral signature from concrete, wood, or glass. The image analyst must select a sufficient number of training sites in each class to represent the variation present within each class in the image. The classification algorithm then uses spectral characteristics of the training sites to classify the remainder of the image. Training sites developed in one scene may or may not be transferrable to an entire study area. If ground conditions, lighting conditions, or atmospheric effects change from scene to another, then training sites must be developed independently for each scene. Furthermore, training sites may not be transferrable across time; in addition to the conditions noted above that change over time as well as space, real changes in the land cover occurring at a training site location over time will cause incorrect classification results in the second image. Accurate supervised classification results depend entirely on the analyst’s ability to collect a sufficient number of training sites and to recognize when training sites can or cannot be transferred from one image to another.
- Unsupervised classification requires less input from the analyst before processing. The classification algorithm searches and analyses the image, grouping pixels into clusters which it deemed to be uniquely representative of the image content. After classification, the image analyst must determine if these arbitrary classes have meaning in the context of the end-user application. A significant amount of time may be spent trying to determine the physical meaning of a class identified by the unsupervised algorithm. In addition, experimentation is required to determine the optimal number of unique classes used for initialization of the algorithm. Furthermore, there is no basis to believe that the classes discovered in one image will be the same classes discovered in a second image. Time spent trying to optimize and interpret the unsupervised results may far exceed the time an analyst would have spent selecting training sites for supervised classification. Finally, because it is impossible to ensure consistency in class identification from one image to the next, unsupervised classification is not useful for change detection.
Classification schemes may be comprised of hard, discrete categories; in other words, each pixel is assigned to one, and only one, class. Fuzzy classification schemes allow a proportional assignment of multiple classes to pixels. The entire image scene may be processed pixel-by-pixel, or the image may be decomposed into homogeneous image patches for object-oriented classification. As stated by Jensen (2005), “no pattern classification method is inherently superior to any other.” It is up to the analyst, using his/her knowledge of the problem set, the study area, the data sources, and the intended use of the results, to determine the most appropriate, efficient, time and cost-effective approach.
Measuring the accuracy of classification requires either comparison with ground truth or comparison with an independent result. Errors of omission are committed when an object is left out of its true class (a tree stand which is not classified as forest, for example); errors of commission are committed when an object that does not belong in a class is incorrectly included (in the example above, the tree stand is incorrectly classified as a wetland).