FINAL FOR NR319
Quiz yourself by thinking what should be in
each of the black spaces below before clicking
on it to display the answer.
Help!
|
|
||||
|---|---|---|---|---|---|
| Advantages of remote sensing | area covered, data format, economy
🗑
|
||||
| additive color | red + blue + green = white computer screen
🗑
|
||||
| subtractive color | yellow + magenta + cyan = black printer
🗑
|
||||
| how are digital images different from photos? | digital is electronic image capture, not chemical digital can be numerically manipulated digital can be resized - rescaled
🗑
|
||||
| Passive (vs. active) | most of the sensors - energy reflected to sensor is from another sources ie sun's energy
🗑
|
||||
| Active (vs. passive) | sensor emits & recieves own energy reflected from a target ie lidar/radar
🗑
|
||||
| platforms of digital remote sensing | airborne or spaceborne
🗑
|
||||
| airborne (vs. spaceborne) | offers high spatial & spectral resolution. can acquire whenever it is needed
🗑
|
||||
| spaceborne (vs. airborne) | sensors provide synoptic overview, often less detailed (lower spatial resolution)
🗑
|
||||
| 4 resolutions of remote sensing | radiometric, spatial, spectral, temporal
🗑
|
||||
| radiomtric | number of separable levels of data recorded digital number - eg. 0 - 255
🗑
|
||||
| spatial | the area of ground covered by each image cell eg. 1 m vs 30 m
🗑
|
||||
| spectral | the specific wavelengths caputred by a sensor
🗑
|
||||
| temporal | time gap between data captures
🗑
|
||||
| spatial resolution - digital images | "size" can change easily (zoom, subset, mosaic)pixel size is assumed not to change scale is referenced to physical size of the pixel ie 1 pixel =30 meters (landsat 7)
🗑
|
||||
| landsat 7 has 7 bands of spectral resoluation | 3 visiable to eye - blue 1, green 2, red 3NIR 4, Mid IR 5, Thermal 6, Mid IR 7
🗑
|
||||
| Temporal Resolution near term | revisit
🗑
|
||||
| Temporal resolution long term | change detection
🗑
|
||||
| 7 principles of photo interpretation | size, shape, shadow, tone/color, texture, patter, association/site/location
🗑
|
||||
| digital number (DN) of each pixel | reflectance or brightness of each pixel
🗑
|
||||
| statistical analysis | the numbers behind the image
🗑
|
||||
| multispectral | pixels & digital numbers pixel (picture cell)eg. landsat 7 1 pixel = 30 m, DN = 0 - 255
🗑
|
||||
| Some important considerations for digital images (1) | Digital images are symmetrical raster datasets consisting of rows (i)& columns (j) that define "n" number of pixels
🗑
|
||||
| Some important considerations for digital images (2) | multispectral images, by their nature, are compressed of more than one band (k)
🗑
|
||||
| Some important considerations for digital images (3) | multiple bands are geometrically "registered" to each other... meaning they overlay each other correctly (pixels aligned)
🗑
|
||||
| false color composites | we only have 3 bands of screen color to display potentially many more bands of information some of which are invisible to the naked eye
🗑
|
||||
| passive optical sensors | spectral vs spatial resolution
🗑
|
||||
| Ikonos | high spatial (4m) low spectral (4 bands)
🗑
|
||||
| landsat MSS | moderate spatial (80m) low spectral 5 bands
🗑
|
||||
| MODIS | low spatial (250-1000m)high spectral 32 bands
🗑
|
||||
| Passive Optical Satellite Platform sensors | Multispectral moderate resolution sensors (landsat)Multispectral Low Spatial Resolution Sensors (AVHRR, MODIS)Multispectral high-spatial resolution sensors (hyperspatial) (IKONOS, Quickbird)
🗑
|
||||
| Landsat info | 1st depolyed in 1972, Currently L5 & L7 in orbit, L7 has 7 spectral bands, 30m pixels (therma 60m, panchromatic 15m) 16 day visit (8 day by L5 or L7) landsat data continuity mission planning for next Landsat, Free imagery
🗑
|
||||
| MODIS | multispectral/multispatial resolution sensor w/ 2 satellites - Aqua & terraGlobal coverage 2x daily
🗑
|
||||
| MODIS uses | surface reflectance, land surface temperature & emissivity, land cover/change, vegetation indices, thermal anomalies/fire
🗑
|
||||
| Image processing | file formats
🗑
|
||||
| Digital remote sensing introduce bands of inormation represented as | digital numbers per pixel
🗑
|
||||
| digital reomte sensing primarily uses | digital data from spaceborne platforms
🗑
|
||||
| we can numberically process digital images in many way | beyond basic visual interpretation
🗑
|
||||
| Landsat program has been the workhorse of remote sensing with | other platforms/sensors providing information at differnt resolutions
🗑
|
||||
| two types of classiciation | supervised & unsupervised
🗑
|
||||
| image enhancement | basic image manipulation
🗑
|
||||
| convolution (neighborhood analysis | involves the passing of a moving window over an image & creating a new image where each pixel in the new image is a function of the original pixel values within the moving window & the coefficents of the moving window as specified by the user
🗑
|
||||
| convolution kernel | a windowmay be considered as matrix (or mask) of coefficients that are to be multiplied by image pixel values to derive a new pixel value for a resultant ehanced image. This matrix may be of any size in pixels & does not necessarily have to be square.
🗑
|
||||
| image enhancement convolution | the shape of the kernel is applied to te image to create a target neighborhoodsum(kernel x image neighborhood)/sum(kernel)
🗑
|
||||
| Spatial filters low pass | a low pas (mean) filter tends to generate the image)
🗑
|
||||
| spatial filters edge | identify gradients/transitions between pixel values such as faults, road cuts outcrops
🗑
|
||||
| image enhancement image ratios (1) | healthy vegetation reflects strongly in the near-infrared portion of the spectrum while absorbing strongly in the visible red.
🗑
|
||||
| image enhancement image ratios (2) | other surface types, such as soil & wter, show near equal reflectances in both the enar-infrared & red portions
🗑
|
||||
| image enhancement image ratios (3) | thus the discrimination of vegetation from other surface cover types in significantly enhanced
🗑
|
||||
| image enhancementvegetation indices/ ratios | SVI = NIR/red NDVI = NIR-red/NIR+red NBR=NIR-MIR(7)/NIR+MIR(7)
🗑
|
||||
| SVI | SIMPLE VEGETATION INDEX
🗑
|
||||
| NDVI | normalized difference vegetation index
🗑
|
||||
| image classification | matching spectral classs to information classes
🗑
|
||||
| image classification definition: | turning data into information
🗑
|
||||
| defined image classification: (1) | 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
🗑
|
||||
| defined image classification: (2) | the objective is to assign all pixels in the image to particular class or themes ( eg water, coniferous forest, deciduous forest, corn, wheat,e tc
🗑
|
||||
| defined image classification: (3) | the resulting classified image is comprised of a mosaic of pixels, each of which belong to a particular theme, and is a thematic map
🗑
|
||||
| defined image classification: (4) | classification can then be tested for accuracy
🗑
|
||||
| defined image classification: (5) | information classes are those categories of interest that the analyst is actually trying to identify in the imagery or at least has field data on
🗑
|
||||
| example of image classes field data | different kinds of crops, different forest types or tree species, differnt geoligical units or rock types, etc.
🗑
|
||||
| defined image classification: (6) | spectral classes are groups of pixels that are uniform (or near-similar) with respect to their brightness values in the image - they tend to cluster around a mean value
🗑
|
||||
| defined image classification: (7) | the objective is to match the spctral classes in the data to the imformation classes of intrest
🗑
|
||||
| defined image classification: (8) | rarely do spectral classes perfectly match information classes
🗑
|
||||
| defined image classification: (9) | a broad information class (eg forest) may contain a number of spectral sub classes with unique spectral variations
🗑
|
||||
| Feature space | plotting reflectance in various wavlengths
🗑
|
||||
| Image classification - supervised | select training fields, evaluate signatures, classify image, evaluate classification
🗑
|
||||
| image classification - unsupervised | run cluster algorithm, classify image, evaluate signatures, evaluate classification
🗑
|
||||
| Unsupervised classification (1) | litterally allowing the comupter to identify statistically similar classes of pixel values=clusters
🗑
|
||||
| Unsupervised classification (2) | does not require prior inforamtion (a priori) of the subject area - meaning you can classify without field data
🗑
|
||||
| Unsupervised classification (3) | you can still have to compare your classification with reality eventually
🗑
|
||||
| Unsupervised classification (4) | a classification scheme must be in place - are you a lumper or splitter?
🗑
|
||||
| Unsupervised classification (5) | you set the number of classes - the computer calculates the appropriate clusters
🗑
|
||||
| Lumping vs splitting too few | numerous cover types sharing spectral classes,
🗑
|
||||
| Lumping vs splitting too many | redunant classes that need to be combined
🗑
|
||||
| Lumping vs splitting | either too many or too few you will need to combine (lump) some classes & break apart (split) others
🗑
|
||||
| Unsupervised classification (6) | no prior knowledge but there needs to be some knoledge or the are to interpret the resulting classes
🗑
|
||||
| Unsupervised classification (7) | the opportunity for input error is minimized
🗑
|
||||
| Unsupervised classification (8) | unique classes are recognised as distinct clusters of unique spectral classes
🗑
|
||||
| Unsupervised classification (9) | challenge is to convert these data classes into accurate information classes
🗑
|
||||
| Unsupervised classification challenges (1) | classes are statistically based - may not match field data well
🗑
|
||||
| Unsupervised classification challenges (2) | spectral classes are not necessarily information classes
🗑
|
||||
| Unsupervised classification challenges (3) | you still have to interpret the classes & decide whether to lump or split classes
🗑
|
||||
| supervised classification (1) | uses representative training sites to direct or train the computer to identify all similar pixels and therefore classes
🗑
|
||||
| supervised classification (2) | requires prior knowledge to determine training sites
🗑
|
||||
| supervised classification (3) | classification algorithum/rule (ie maximum likelihood) is chosen and training sites applied
🗑
|
||||
| supervised classification (4) | results are compared to known field data & accuracy assessed
🗑
|
||||
| four main stages involved in supervised classification are: | training stage, classification stage, output stage, accuracy assessment stage
🗑
|
||||
| image enhancement summary I (1) | digital imagery allows a wide variety of enhancements to be applied
🗑
|
||||
| image enhancement summary I (2) | "simple" acts of zooming, subsetting, filtering provide improved image analysis - compare that to traditional photo
🗑
|
||||
| image enhancement summary I (3) | classification is the process of organizing the pixels of an image into similar value classes
🗑
|
||||
| image enhancement summary I (4) | the key to classification is turning pixel values into information
🗑
|
||||
| image enhancement summary I (5) | the 2 fundamental types of classification are unsupervised & supervised
🗑
|
||||
| image enhancement summary I (6) | unsupervised requires no prior knowledge of an area and its cover types
🗑
|
||||
| image enhancement summary I (7) | supervised requries prior knowledge through the use of training areas
🗑
|
||||
| image enhancement summary I (8) | remember, remote sensing does not occur in a vacuum - you have to bring knowldge & experience to the analysis to be effective & accurate
🗑
|
||||
| image enhancement summary II (1) | remote sensing allows the acquisition of information about a place through interpretation & measurment of images
🗑
|
||||
| image enhancement summary II (2) | GIS & remote sensing are inherently tied together
🗑
|
||||
|
🗑
|
Review the information in the table. When you are ready to quiz yourself you can hide individual columns or the entire table. Then you can click on the empty cells to reveal the answer. Try to recall what will be displayed before clicking the empty cell.
To hide a column, click on the column name.
To hide the entire table, click on the "Hide All" button.
You may also shuffle the rows of the table by clicking on the "Shuffle" button.
Or sort by any of the columns using the down arrow next to any column heading.
If you know all the data on any row, you can temporarily remove it by tapping the trash can to the right of the row.
To hide a column, click on the column name.
To hide the entire table, click on the "Hide All" button.
You may also shuffle the rows of the table by clicking on the "Shuffle" button.
Or sort by any of the columns using the down arrow next to any column heading.
If you know all the data on any row, you can temporarily remove it by tapping the trash can to the right of the row.
Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.
Normal Size Small Size show me how
Normal Size Small Size show me how
Created by:
missMRWhite
Popular Science sets