Chapter Contents
Object-Based Image Analysis (OBIA)
Classification Accuracy Assessment
Image classification is the processes of grouping image pixels into classes of similar types. A typical example of using image classification is the land cover identification from remotely sensed images. This chapter focuses on land cover classification techniques.
Introduction
Land Cover Classification Scheme
There are many categories or classes that we can derive from an image. There are some image classification schemes frequently used. The most popular landuse/landcover classification scheme is the Anderson’s classification scheme developed by the U.S. Geological Survey in 1976. It uses a hierarchical structure in multiple levels. Level I has nine categories as shown in Figure 1. Level II has more detailed categories, and, with further details, the Anderson’s classification scheme goes down up to Level IV. Figure I shows that Forested Land, for example, is further divided into three classes in Level II.
Figure 1. USGS land cover classification scheme by Anderson, et al. (1976)
Patterns used for Image Classification
Three patterns in an image are frequently used for image classification – spectral pattern, temporal pattern, and spatial pattern. With a multispectral dataset, we can classify pixels by examining the spectral profile in each pixel. With a multitemporal dataset, temporal patterns can also be analyzed to determine the category of a pixel. In this case, plant phenology is frequently used. Plant phenology is the study of the annual cycles of plants and how they respond to seasonal changes in their environment. For example, there will be little greenness on deciduous trees in winter, but high greenness levels during summer. Spatial patterns are also used to classify an image. Particularly, texture, size, shape, and directionality are frequently used. Figure 2 shows a Landsat 8 OLI image and a spectral pattern on a wooded land. Band 5 (i.e. the near infrared band) shows very high reflectance compared with other bands.
Figure 2. A spectral pattern of a wooded land in Atlanta, GA. Landsat 8 OLI ARD image. (May 6, 2020)
Classification Approaches
There are four classification approaches frequently used as shown below.
· Pixel-based image classification
o Unsupervised
o Supervised
· Object-based image classification
o Unsupervised
o Supervised
The pixel-based approach clusters pixels using their spectral or temporal profile. The object-based image analysis (OBIA) creates polygonal objects by clustering neighboring pixels by looking at their statistical properties such as texture, size, shape, or directionality. Both approaches can be combined with either unsupervised or supervised approaches.
As shown in Figure 3, unsupervised classification classifies pixels into groups with iterative statistical algorithms, and then, the meaning of each group is later identified in relation to ground features. Supervised classification uses a different approach. First, statistical signatures are derived from a training dataset. And then, each pixel is compared with statistical signatures to find out the best-fit category. Eventually, the category of the best-fit signatures is assigned to the pixel.
Figure 3. Unsupervised vs. supervised image classification procedures.
Creating a Training Dataset
A training dataset needs to be created carefully. A training dataset is composed of multiple training samples. Training samples must clearly represent the land cover classes of interest. Each training sample site needs to be homogeneous.
Enough pixels need to be collected for each class. The Minimum in practice is [10 x # of bands]. For example, if seven bands from the OLI sensor are used for image classification, at least 70 pixels needs to be collected for each class.
Training samples can be collected from a field survey (i.e. in situ collection) or from a screen digitizing. Each polygonal area delineated by screen digitizing is called AOI (area of interest) or ROI (region of interest). Figure 4 shows training samples in different colors. The training dataset was used for the supervised classifications described in this chapter.
Figure 4. Training samples in a training dataset.
Classification Tools
Many software packages provide image classification tools. With the development of machine learning algorithms, various image classification algorithms have been developed too. The following lists popular software packages that provides advanced image classification methods:
· Opensource
o SAGA. http://www.saga-gis.org/en/index.html
o The Caret package for R. http://topepo.github.io/caret/index.html
o OpenCV and TensorFlow in Python. https://www.python.org/
o WEKA for ImageJ. https://imagej.net/Trainable_Segmentation
· Commercial
o ERDAS Imagine. https://www.hexagongeospatial.com/products/power-portfolio/erdas-imagine
o ENVI. https://www.l3harrisgeospatial.com/Software-Technology/ENVI
The examples presented in this chapter were developed with the SAGA (System for Automated Geoscientific Analysis) Version 7.7.0 that provides the following image classification tools:
· K-Means Clustering for Grids
· ISODATA Clustering
· Decision Tree
· Supervised Classification for Grids
o Binary Encoding
o Parallelepiped
o Minimum Distance
o Mahalanobis Distance
o Maximum Likelihood
o Spectral Angle Mapping
o Winner Takes All
· Supervised Classification for Shapes
· Supervised Classification for Tables
· Seeded Region Growing
· Superpixel Segmentation
· Watershed Segmentation
· Object Based Image Segmentation
· SVM (Support Vector Machine) Classification
· Artificial Neural Network Classification (OpenCV)
· Boosting Classification (OpenCV)
· Decision Tree Classification (OpenCV)
· K-Nearest Neighbours Classification (OpenCV)
· Normal Bayes Classification (OpenCV)
· Random Forest Classification (OpenCV)
· Random Forest Table Classification (ViGrA)
K-Means Clustering
The K-means clustering is a pixel-based unsupervised classification method. It starts by partitioning the input pixels into k initial clusters, either at random or using some heuristic data. It then calculates the mean of each cluster. It constructs a new partition by associating each pixel with the closest mean. Then the mean values are recalculated for the new clusters, and the algorithm is repeated by applying these two steps until convergence, which is obtained when the pixels no longer switch clusters (or alternatively, mean values are no longer changed). Figure 5 shows the output of K-means clustering with 4 categories.
Once pixels are grouped into clusters, the clusters need to be named to reflect ground features. In this procedure, multiple clusters can be combined to one. For example, the clusters in gray, red, and sand colors in Figure 5 may be combined to the built-up class that includes roads, buildings, and pavements.
Figure 5. K-means clustering with 4 classes.
ISODATA Clustering
The ISODATA clustering is another pixel-based unsupervised classification method. ISODATA stands for iterative self-organizing data analysis technique. The ISODATA clustering algorithm has some further refinements by splitting and merging of clusters. Clusters are merged if either the number of pixels in a cluster is less than a certain threshold or if the centers of two clusters are closer than a certain threshold. Clusters are split into two different clusters if the cluster standard deviation exceeds a predefined value and the number of pixels is twice the threshold for the minimum number of members. Unlike the k-means algorithm, the ISODATA algorithm allows for a different number of clusters in output. Figure 6 shows the result of ISODATA clustering.
Figure 6. ISODATA clustering with 4 classes.
Minimum Distance Classifier
The minimum distance method is a supervised image classification technique. In the minimum distance method, the distance between the mean of the signature dataset and the pixel value is calculated band-by-band. Then, a total distance is calculated. This total-distance calculation routine is repeated with all class signatures. The class that shows the minimum total distance is assigned to the pixel. This minimum distance method is very fast and simple in its concept. Figure 6 shows the result of minimum distance classification.
Figure 6. Minimum distance classifier
Maximum Likelihood Classifier
The maximum likelihood classifier (MLC) considers statistical probability of the class signatures when deciding the class that a pixel belongs to. The maximum likelihood method should be used Carefully. First, sufficient training samples should be used with this method. Second, if there are very high correlations among bands or if the classes in the training dataset are very homogeneous, results are unstable. In such cases, users may consider reducing the number of bands using the principal component transformation. Third, when the distribution of training samples does not follow the normal distribution, the maximum likelihood method will not perform well. Figure 7 shows the result of MLC classification. Paved land covers like roads and parking lots are classified to Buildings. Also, residential areas are classified to Grasses.
Figure 7. Maximum likelihood classifier.
Decision Tree Classifier
The decision tree method is a supervised classification technique that uses a training dataset. A decision tree is a connected graph with a root node and one or more leaf nodes in a tree shape. A decision tree can be built manually, or with machine learning from a training dataset. Once a decision tree is created, pixels can be classified using it. Figure 8 shows the result of applying decision tree classification method. The figure shows that multiple roads are confused with grasses. Also, pavements are not well separated from buildings.
Figure 8. Decision tree classifier.
Random Forest Classifier
The random forest method is a supervised classification technique and uses a machine learning algorithm. As a frequently used method, the random forest classifier uses multiple decision trees, making a forest. Figure 9 shows the result of applying the random forest classifier with the following parameters, and the result is similar to the decision tree classifier.
· Maximum tree depth: 10
· Minimum sample count: 2
· Maximum categories: 6
· Use 1SE rule: No
· Truncate pruned trees: no
Figure 9. Random forest classifier.
Neural Network Classifier
The neural network classifier is a supervised classification method that uses machine learning algorithms. Figure 10 shows an output using the neural network classifier with the following parameters:
· Number of layers: 3
· Number of neurons: 6
· Maximum number of iterations: 300
· Error change (epsilon): 0.0000001192
· Activation function: Sigmoid (alpha = 1, beta = 1)
· Training method: back propagation (weight gradient term = 0.1, moment term = 0.1)
Figure 10. Neural network classifier.
Object-Based Image Analysis (OBIA)
OBIA starts with segmenting an image into multiple polygons, i.e. objects, by grouping pixels using such information as shape, texture, spectral properties, and geographic proximity. The polygons are further merged into classes using supervised or unsupervised clustering methods. Figure 11 shows polygon objects, and Figure 12 shows the result of unsupervised OBIA classification with the following parameters:
· Band width for seed point generation: 2
· Neighborhood determination: 4 direction (Neumann neighborhood)
· Distance determination: Feature space and position
o Variance in feature space: 1
o Variance in position space: 1
o Similarity threshold: 0
· Generalization: No generalization
· Number of unsupervised classification classes: 6
Figure 11. Polygon objects
Figure 12. OBIA classification, unsupervised.
Classification Accuracy Assessment
Once an image is classified, the accuracy of the classification should be assessed. An error matrix is frequently used for checking errors. The error matrix is also known as a “contingency table,” or “confusion matrix.” Enough cases should be sampled to test classification errors. In general, 50 cases per class are recommended. If the study area is larger than 1 million acres, or if there are more than 12 categories, 75-100 cases per category are recommended. When selecting sample cases, random or stratified random sampling would bring less biased assessment. Testing results are organized as shown in Table 1. Columns represent classification results, and rows represent reference data. The value of 64 in the table, for example, means that 64 Water pixels in reality were classified to Conifer, which means an error.
Table 1. An error matrix (example)
When assessing classification accuracies, we need to check them from two standpoints – producer’s and user’s. From a producer’s standpoint, what is important is how well the original features are represented in the classification output. This is what we call producer’s accuracy. Producer’s accuracy is calculated in association with reference data. However, in a user’s perspective, what is important is how much the classification results are accurate. This is, what we call, user’s accuracy. The user’s accuracy is calculated in association with classification results. Table 2 shows how to calculate producer’s accuracy, user’s accuracy, and overall accuracy. In the table, the capital letters A – P indicate numbers.
Table 2. Accuracy calculation
Table 3 shows an example of producer’s accuracy and user’s accuracy. For example, 92.51% of urban areas are represented in the classification result, and only 84.54% of urban pixels are truly urban. The overall accuracy is 97.6%.
Even if the overall accuracy is a good indicator of an image classification accuracy, the overall accuracy may change if another sample dataset is used.
The KHAT statistic takes potential sampling errors into account, so that KHAT is more robust than the overall accuracy. KHAT and the overall accuracy range from 0.0 to 1.0. Conventionally, the KHAT values less than 0.4 are considered a very poor classification.
Table 3. An accuracy test result (example)
References
James R. Anderson, Ernest E. Hardy, John T. Roach, and Richard E. Witmer, 1976. A land use and land cover classification system for use with remote sensor data. Professional Paper 964 (A revision of the land use classification system in Circular 671). https://doi.org/10.3133/pp964.