The goal of this lab exercise is to gain a better understanding of two different, more advanced classification algorithms. In more advanced classification methods, rather than more basic methods and supervised classification, uses algorithms which are used in order to increase the overall classification. Throughout this lab I will be using expert system/decision tree classification using ancillary data and also develop an artificial neural network in order to perform a more advanced classification of an image.
Methods
Expert System Classification
Expert system classification is a very effective method of classification which uses both spectral data and ancillary data. This method of image classification is usually used to improve spectral images which have already been classified. There are two main steps in the process of expert system classification: building a knowledge engineer/knowledge base which is used in order to train a knowledge classifier and then using the built knowledge engineer to classify an already classified image which needs to be improved. In order to increase the classification accuracy other ancillary data can also be used in this second step.
In order to produce a knowledge engineer. This is a tool which is available in ERDAS Imagine 2010 and once the interface is opened there are three main components used in the engineering process. The first are hypotheses which are used to target LULC (land use/land cover) classes which you plan to produce. Rules are then used as a method of communication between the original classified image and the ancillary data through the use of a function. Variables include both the original classified image and the ancillary data. To perform an expert system classification using the knowledge engineer interface, I first created a hypothesis which will LULC of the original classified image. Then I will add a rule which will apply variables (the original classification image) which are to be applied to the hypothesis ID. Once the variable prop was applied I changed the value of water from 0 to 1 since the water feature class in this first example has a value of 1 in the image which was used as the variable in this process (Fig. 1).
(Fig. 1) The use of the knowledge engineer tool to apply hypotheses and rules to provide more advanced LULC classification.
This process was then applied to the rest of the LULC classes in the original image. The next step was to use ancillary data to develop a knowledge base. Based on the qualitative analysis I conducted on the original classified image I noticed that there were some errors in the original classification. For this reason it was important to use ancillary data in order to properly classify the regions which showed errors. One example of how I separated these overlapping classes was by dividing the urban LULC class into residential and other urban LULC classes. To do this I created a new hypothesis and a new variable within the new rule which corresponded to my hypothesis. This is where I inputted the ancillary data. Once this new hypothesis and rule have been created I made the argument based on ancillary data to the previous classified image. To do this I added a reciprocal/counter argument on the urban LULC class which will cause the separation of the original class. The same process was done for agriculture and green vegetation as well as green vegetation and agriculture. The final knowledge engineer file contained 8 different hypotheses (Fig. 2).
(Fig. 2) The final knowledge engineer includes a total of 8 hypotheses used to produce the expert system classification.
After the knowledge engineer file was saved, I performed the expert system classification. To do this I opened the knowledge classifier tool and inputted the knowledge file I created. Once that was done I was able to produce a final output image and compare it to the original classified image (Fig. 3).
(Fig. 3) This image shows the original classification image is shown on the left while the image on the right is the re-classified image produced from the knowledge engineer.
Neural Network Classification
Neural network classification is a method of image classification which acts similar to the process of the human brain in order to develop LULC classes. ANN, or artificial neural network uses ancillary data along with reflective remotely sensed data in order to develop weights in hidden layers of the image. Within this process I would train a neural network in order to perform the image classification in the study area, which in this lab is of the University of Northern Iowa campus.
The first portion of the lab I performed neural network classification using predefined training samples. To do this I used the program ENVI to restore predefined ROIs (regions of interest) to the image using the ROI tool. These ROIs are what I used to later train the neural network. To train the neural network in order to perform the classification I selected classification-supervised-neural network. I then selected the 3 ROIs, the logistic radio button and then changed the number of training iterations to 1000 for training. Once this is done I could study the output image and determine how accurate the LULC classification was.
Results
The expert system classification proved to be a much more accurate classification method compared to previous methods I have used in other labs. Because of the application of ancillary data the LULC class could more accurately be determined and represented in the study area. My initial application of the neural network classification using predefined training samples was not very accurate. However, the more I increased the training rate and the number of iterations the accuracy increased.
Sources
The data used in this lab was collected from the following sources: Landsat satellite images from Earth Resources Observation and Science Center and USGS. Quickbird high resolution image of portion of University of Northern Iowa campus from Department of Geography at the University of Northern Iowa. All data was provided by Dr. Cyril Wilson of the University of Wisconsin- Eau Claire.
No comments:
Post a Comment