遥感实验作业

东北大学遥感实验报告学号：20141668学生姓名：古再丽努尔喀日指导教师：包妮沙专业班级：测绘1402院（部）：资源与土木学院2016年4月Experiment 3 Image ClassificationExperiment 3: image classification1 Experiment data：Subset Landsat imagery（TM/ETM/OLI）2 Software：ENVI5.1.3 Aims：Supervised and Unsupervised classification.4 Principles: supervised classification unsupervised classification： The supervised classification is the essential tool used for extracting quantitative information from remotely sensed image dat. Using this method, the analyst has available sufficient known pixels to generate representative parameters for each class of interest. This step is called training. Once trained, the classifier is then used to attach labels to all the image pixels according to the trained parameters. The most commonly used supervised classification is maximum likelihood classification (MLC), which assumes that each spectral class can be described by a multivariate normal distribution. Therefore, MCL takes advantage of both the mean vectors and the multivariate spreads of each class, and can identify those elongated classes. However, the effectiveness of maximum likelihood classification depends on reasonably accurate estimation of the mean vector m and the covariance matrix for each spectral class data. Whats more, it assumes that the classes are distributed unmoral in multivariate space. When the classes are multimodal distributed, we cannot get accurate results. Another broad of classification is unsupervised classification. It doesnt require human to have the foreknowledge of the classes, and mainly using some clustering algorithm to classify an image data. These procedures can be used to determine the number and location of the unimodal spectral classes. One of the most commonly used unsupervised classifications is the migrating means clustering classifier (MMC). This method is based on labeling each pixel to unknown cluster centers and then moving from one cluster center to another in a way that the SSE measure of the preceding section is reduced data. This project performs maximum likelihood supervised classification and migrating means clustering unsupervised classification to an AVHRR Local Area Coverage (LAC) Data image, and compares the results of these two methods. In addition, using the results of MMC to train the MLC classifier is also shown and will be compared together. 5 Results1) The evaluation of training area for supervised classification 2) The supervised classification map and accuracy assessment supervised acc PS:1:ocean 2:river 3:build-up 4:road 5:residential 6:crap land 7：vegetation 8:lack 9:forest 10:grassland 12345678910total171484029000010071514203520610300110041739914945066401451052614014271027368311711241750116055392951871551018611221185773263021644271013010261007331638048317012823001284690221468101059157041210146146134340511202489total71599402525112275964061061393119021398979pixel/accuracy Prod accuracy (%)User accuracy (%)199.8499.96287.5684.41394.1793.99483.6198.46571.0152.95690.1299.62794.3491.35892.0599.82982.6383.941094.8493.53 3) The unsupervised classification map and accuracy assessment Unsupervised accPS:1:ocean 2:river 3:build-up 4:road 5:residential 6:crap land 7：vegetation 8:lack 9:forest 10:grasslandpixel/accuracy Prod accuracy (%)User accuracy (%)189.6686.67276.8287.41393.6591.13463.4568.23576.4075.23689.8887.11788.9887.23897.2397.87987.2294.341088.9878.124) Compare the land cover between supervised classification and unsupervised classification Unsupervised classificationis where the outcomes land cover is based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the land cover.Supervised classificationis based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.so land cover is selected based on the knowledge of the user. The user also sets the bounds for howsimilar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on brightness or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. Many analysts use a combination of supervised and unsupervised classification processes to develop final out put analysis and classified maps.6 Summarize: This is the most meaningful experience I have ever did. I am used to do for a good mark and treat it as a homework , but this experiment is extremely different , because since I found I am getting interested of remote sensing ,besides I am an English lover ,I finished this experience totally by myself. Of course I got a lot of problems during this task , when I did unsupervised classification I asked my classmates and my roomies , so I appreciate them . for finish this experience ,I reviewed this chapter ,besides I checked a lot of materials ,so this is a bi