Classification Advantages of Visual Interpretation 1 Human brain

Classification Advantages of Visual Interpretation: 1. Human brain is the best data processor for information extraction, thus can do much more complex operations that computers can not match. 2. Interpreters expertise can be part of the input Disadvantages: 1. We can only see three bands at a time. Can not process more at a time. 2. Time consuming and costly. Can not be automated, thus not applicable for large projects. 3. Requires training and experience to do a good work.

Computer-Aided Classification Parametric: assumes normal distribution of the data Supervised classification: provide the computer with some examples of known features in multi-dimensional feature space. The computer will first analyze the statistical parameters for the training data and then assign all other pixels to one of the classes in the examples based on statistical similarity. Unsupervised classification: Instead of providing the computer with examples of features in multi-dimensional feature space, the users let the computer to identify pre-specified number of spectral clusters among which the difference between clusters are maximized and within clusters are minimized. It is the users’ responsibility to assign a class label to each of the clusters. Note: one class may have many clusters. Hybrid Classification: It takes the advantage of both the supervised classification and unsupervised classification. 1. Collect training sets. 2. Unsupervised classification to identify spectral clusters within the training sets. 3. Classify image with the clusters. 4. Regroup the clusters into original classes

Advantages of Computer-Aided Classifications 1. Can be automated for large area application 2. Better consistency 3. Can process as many images with as many bands as necessary 4. Though image processing experience are required for classification, the analysts ground experience for what on the ground looks like in the image with certain band combination are not required.

Supervised Classification Algorithms 1. Minimum-distance-to-means classifier The training set provides a mean value for each class for each band, the mean vector. Each pixel is assigned to a class to which it has the shorted Euclidean distance. Band y u u u u w wwww w u u u ssss u u f f c c c f f f Band x Advantage: mathematically simple and computationally efficient. Disadvantage: Not sensitive to degrees of variation for different classes. For example the triangle point will be assigned to s class while it really is u class. Because of this, it is not widely used.

Parallelpiped Band y u u u u w wwww w u u ssss u u u f f c c c f f f Band x The rules to assign a pixel into a class are determined by the regions that are spanned by the DN ranges of each category as shown above. The rectangular regions are called parallelpipeds. The variance associated with each category are considered. Pixels falling outside of the parallelpipeds belong to “unknown” category and Pixels falling in the overlap area are “not sure” or arbitrarily assigned to one of the two overlapping categories. Advantages: fast and efficient computationally. Disadvantages: overlapping of decision regions causes problems because of high correlation among DN values across bands for different features.

Maximum Likelihood Classification Assumptions: DN values for each class are normally distributed -3 Class mean Standard deviation -2 - + 68. 3% +2 +3 95. 5% 99. 7% If a pixel falls outside the range of ( +/-3 ), we can say that the probability that this pixel belongs to the class is 0. 3%, or we can say we are 99. 7% sure that this pixels does not belong to this class.

Maximum Likelihood Classification In a two dimensional spectral space, each class form a probability density region, the probability for a pixel belongs to a center category is defined by its distance measured in standard deviation. The pixel is assigned a class that is most likely (maximum likelihood). Disadvantages: Large number of computations, particularly many spectral bands are involved.

Unsupervised Classification Unsupervised classification does not use training data as the basis for classification, but examine unknown pixels in an image and aggregate them into a number of clusters based on natural groupings present in the image values. The classification assumes that DN values within a given cover type should be close together in the spectral space, while data in different classes should be comparatively well separated. The categories that unsupervised classification identify are not land cover or use classes, but spectral classes or clusters. Some cover types may have encompass multiple clusters, for example, agriculture land may have tobacco, soy beans, sugar cane, rice, wheat etc. But they look different spectrally. The analysts must provide labels to each of the clusters after unsupervised classification using other sources of data.

Unsupervised Classification Algorithms 1. Draw a 45 degree line and divide into the number of classes by the user. The center of each segment is the mean for the classes Class intervals 5 2. Assign the pixels to each of the classes based on minimum distance rule. 4 Band y The K-means approach: Recalculate the mean center values for each cluster and reassign the class membership for each pixel 4. Repeat step 3 until a pre-specified percentage of pixels does not change class membership. Mean centers 1 2 3 3. Band x

K-means Approach iterations Iteration of recalculating the means causes the mean points migrate to new locations of cluster center

Nonparametric Classification-Neural Networks Neural network classifies images in analogous to neural nerves. It does not assume normality for the training sets, but rather it is learning based. The neural network memorizes what it sees or learns from the training data and then assign the class membership to each pixel in the image. Output layer (classes) Hidden layer (F 2 node) Input layer (images)

Hybrid Classification Step 1. Collect training sets as in the supervised classification. Step 2. Perform unsupervised classification on the training sets to get subclasses for each class. Step 3. Perform supervised classification with the subclasses. Step 4. Aggregate spectral subclasses in the classified images back into the original classes The above process is also called guided clustering. The advantage of the guided clustering is that it allows very different spectral clusters to represent the same class.

Hybrid Classification agriculture Hybrid classification allows these diverse subclass spectral clusters to aggregate to form into subclasses, enhancing classification accuracy. Since we already know the class membership for each of the clusters, it is trivial to put them back into the same class afterwards.

Post Classification Fine Tuning: reclassification 1. Taking out trouble classes or clusters (usually done by masking). 2. Perform classification (usually unsupervised classification) to create new small classes or (clusters). Because the majority of the data is masked out from confusion. Better or cleaner small classes can be generated. 3. Merge the re-classification with the majority of data. 4. Sometimes, it may take a few iterations to achieve a satisfactory result.

Post Classification Fine Tuning-- Map Editing The last step in classification is map editing which is often not mentioned. The following steps may be required. 1. Smoothing: Using spatial filters to remove the “salt and pepper” effect that is caused by isolated pixels in the middle of a large continuous class. This can be done by running the following filters: a) mean filter b) majority filter c) medium filter d) sieve 2. Hand editing to correct the obviously misclassified pixels This may be very time consuming. But improves map accuracy.

Land use/Land cover Classification Land cover: the type physcial feature present on the surface of the Earth. For example, corn fields, lakes, forests, concrete highways. Land use: refers to human activity or economic function associated with a specific piece of land. For example, land use of agriculture can include corn, rice, sugar cane, tobacco, orchards, … and so on, all of which are different land cover types. A knowledge of both land use and land cover can be important for land planning and land management activities. Ideally, land use and land cover should be presented on separate maps. In practice, it is often most efficient to mix the two systems when remote sensing data for the principal data source for such mapping activities.

Criteria for USGS Landuse/landcover Classification System 1. 2. 3. The minimum interpretation accuracy with remotely sensed data is >=85% Accuracy of interpretation for several categories should be equal. Repeatable results from one interpreter to another and from one time of sensing to another. 4. Applicable to extensive areas 5. Categorization permit land use be inferred from the land cover types 6. Suitable for use with remotely sensed data obtained at different times of the year. 7. Categories can be divided into more detailed subcagories that can be obtained from large scale imagery or ground surveys. 8. Aggregation of categories be possible 9. Comparison with future land use and land cover data should be possible 10. Multiple uses of land should be recognized when possible. Note: These criteria were setup prior to the widespread use of satellite imagery and computer-aided classification. While most of the 10 criteria withstood the test of time, the first two criteria are not always attainable when mapping land use and land cover large, complex geographic areas.

USGS Landuse/Landcover Classification System for Use with Remotely Sensed Data

Value IGBP Land Cover Description 0 Water Bodies 1 Evergreen Needleleaf Forest 2 Evergreen Broadleaf Forest 3 Deciduous Needleleaf Forest 4 Deciduous Broadleaf Forest 5 Mixed Forest 6 Closed Shrublands 7 Open Shrublands 8 Woody Savannas 9 Savannas 10 Grasslands 11 Permanent Wetlands 12 Croplands 13 Urban and Built-Up 14 Cropland/Natural Vegetation Mosaic 15 Permanent Snow and Ice 16 Barren or Sparsely Vegetated 17 Unclassified