Introduction to Remote Sensing Lecture 11

1. Introduction to Remote SensingLecture 11

2. Remote Sensing So far, we have aimed to answer the following questions: Why use the technique (remote sensing)? What is the physical basis ? How are the data collected ? What range of sensors are there? How can we enhance data ? The question to be answered in the next 2 lectures is: How can we produce thematic maps ?

5. Image Classification Image Classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information The objective is to assign all pixels in the image to particular classes or themes (e.g. water, coniferous forest, deciduous forest, corn, wheat, etc.). The resulting classified image is comprised of a mosaic of pixels, each of which belong to a particular theme, and is essentially a thematic "map" of the original image.

7. Spectral or Information Classes ? When talking about classes, we need to distinguish between Information classes (e.g. land use) Spectral classes (e.g. land cover)

8. Information & Spectral Classes Information classes are those categories of interest that the analyst is actually trying to identify in the imagery, such as: different kinds of crops, different forest types or tree species, different geologic units or rock types, etc. Spectral classes are groups of pixels that are uniform (or near-similar) with respect to their brightness values in the different spectral channels of the data. The objective is to match the spectral classes in the data to the information classes of interest.

9. Information & Spectral Classes Rarely is there a simple one-to-one match between these two types of classes. Rather, unique spectral classes may appear which do not necessarily correspond to any information class of particular use or interest to the analyst. Alternatively, a broad information class (e.g. forest) may contain a number of spectral sub-classes with unique spectral variations. It is the analyst's job to decide on the utility of the different spectral classes and their correspondence to useful information classes.

10. Technicality Assignment of spectral classes to information classes A key process in land-cover mapping is the aggradation of spectral classes, and their assignment to information classes (especially in the case of unsupervised methods) For example, accurate classification of the class deciduous forest may require several spectral sub-classes, such as north-facing forest, south-facing forest, shadowed forest, and the like. When the classification is complete, these sub-classes should be assigned a common symbol to represent the single informational class

14. Churn Farm Remote Sensing data were collected on 4 June 1984 using the NERC ATM scanner for Churn Farm, Berkshire. The ATM scanner that was used has 12 optical bands. Seven of these correspond to Landsat TM wavelengths, the other 5 are experimental bands. The aeroplane carrying the ATM scanner was flown at a low altitude and the image has a spatial resolution of 5 meters.

16. Technicality Selection of Images Generally, the success of a land cover classification can lie in the astute selection of imagery with respect to season and date. Therefore the seemingly mundane process of searching image archives for suitable data assumes vital significance......stemming from the need to answer questions such as: What season will provide the optimum contrasts between classes to be mapped?

17. Technical Detail Details about the Churn Farm data 1. Collected 4 June 1984 2. Grass appears to consist of 2 different varieties, and 2 of the smaller wheat fields look distinct from the rest. 3. The peas have only recently been planted, and much bare soil will be showing through. 4. There is a small amount of cloud in the upper left part of the scene. 5. The Urban areas consist mainly of farm houses and farm yards; there is also an electricity sub-station. In most of these units, there will be several pixels that are pure grass or trees.

21. Classification Types Common classification procedures can be broken down into two broad subdivisions based on the method used: Supervised classification and Unsupervised classification

25. Technicality Land cover can be mapped from remote sensing using a range of classification techniques. In principal, the process is straightforward; in practice, many of the most significant factors are concealed among apparently routine considerations.

27. Supervised Classification Objective: To automatically categorize all pixels in an image into information classes. requires an analysis of the spectral properties of surface features in a multi-band image; and a systematic sorting, based on mathematical decision rules, of the spectral data into spectral/textural categories. Assumption: different surface features manifest different combinations of digital values based on their spectral reflectance/emittance/backscatter properties.

28. Supervised Classification Definition of Information Classes Training/Calibration Site Selection delineate areas of known identify on the digital image Generation of Statistical Parameters define the unique spectral characteristics (signatures) Classification assignment of �unknown� pixels to the appropriate information class Accuracy Assessment test/validation data for accuracy assessment Output Stage

29. Training Stage Objective To assemble a set of statistics that describe the spectral response pattern for each information class. Involves the delineation of areas of known identify on the digital image. Requires close interaction between the analyst and the image data reference data

30. The Training Stage For an accurate classification, training/calibration data must be: Representative to adequately sample the spectral variation for the information class; sample numerous small areas scattered throughout the image; Complete a sufficient sample size is required to ensure accurate statistical descriptors

33. Training Stage - Technicality In reality, therefore, it can be useful if the operator is familiar with the location from which the remotely sensed data has been acquired. This will make the selection of training sites relatively straightforward. In addition, any in-situ spectral measurements of the training areas taken at the time of data collection will be taken into account.

34. Training Sites � Factors to Consider (1) Number of Training/Calibration Areas depends on (i) # of classes; and (ii) diversity of classes many smaller areas better than a few large areas Number of Training/Calibration Pixels 10N to 100N pixels; where n = # of spectral bands (Lillesand and Kiefer) depends on the environment, but at least 100+ pixels per class accumulated from several training areas (Campbell) >10N pixels where n = no. of spectral bands (Jensen)

36. Training Sites � Factors to Consider (2) Size large enough to provide accurate estimates of each information class not too large to result in undesirable variation Shape not important squares, rectangles (right-angled shapes easy to work with)

37. Training Sites � Factors to Consider (3) Location / Placement several training areas throughout the image (use maps and airphotos if field visit not possible relate to recognizable ground features keep away from boundaries Uniformity should be homogeneous (unimodal) rather than heterogeneous (bimodal or multimodal)

39. Technicality Selection of Training Data Accurate selection of training data is crucial for accurate supervised classification. There are many approaches to signature collection and analysis, but all rely to a certain degree on the experience of the analyst.