1. Principles of Remote Sensing

2. Contents Some Basic concepts of remote sensing
Satellite Remote Sensing
Collect the data
Some Image processing
Application

3. 1. Fundamentals of remote sensing
Remote sensing is defined as the technique of obtaining information about objects through the analysis of data collected by special instruments that are not in physical contact with the objects of investigation.
Energy source or illumination (A).
Radiation and the Atmosphere (B).
Interaction with the target (c).
Recording of energy by the sensor (D).
Transmission, Reception and processing (E).
Interpretation and Analysis (F)
Application (G):

4. Electromagnetic Energy Interaction in the Atmosphere
1- Absorption: Among the numerous gases of the atmosphere, the most significant absorbers of EM energy are oxygen (O2), nitrogen (N2), ozone (O3), carbon dioxide (CO2); and water (H2O). The atmosphere's gases are selective absorbers to wavelength [1].

6. Scattering: - Selective scattering: is two types
Rayleigh scatter: the dimensions of the scatters are small than the wavelengths of the electromagnetic radiation.
• Mie scatter: the aerosols in the atmosphere are approximately the same as the wavelengths of the electromagnetic radiation.

7. Scattering:
Non- Selective Scattering: Non- selective scattering becomes operative when the lower atmosphere, so that scattering at all wavelengths occurs equally with aerosols dimensions are greater than approximately ten times the wavelength of the radiation[1]

8. Electromagnetic Energy Interaction with Earth Surface Feature
Objects sensed interact differently to incident energy according to their physical or chemical properties. When EMR strikes a surface, it may be reflected, scattered, absorbed or transmitted can be expressed in the following manner [1 ]:
EI (λ) = ET(λ) + ER(λ) + EA(λ)
ET(λ)=0 a becomes:
ER(λ) + EA(λ) =1= EI(λ)

9. Spectral Reflectance
The physical and chemical characteristics of materials define their reflectance and emittance spectra that can be used identify them.
The spectral reflectance refers to the ratio of object radiant energy reflected to that incident on object.
The spectral characteristics of various earth surface features change with geographic location and time.
Temporal change in spectral response can either natural or caused by human beings. Remote sensing change detection techniques can be used to monitor these temporal changes.

10. Spectral Reflectance for Water and Soil
Spectral Reflectance for Soil: Spectral reflectance of soil generally increases with increasing wavelength.
spectral of water: The reflectance of clear water is generally low.

11. Spectral Reflectance for Vegetation
the reflectance spectra of dry grass and green grass can be distinguished; so that the reflectance of green grass has high reflectance in near-infrared region and low reflectance in visible region, but dry grass has higher reflectance in visible region and lower reflectance in the near-infrared region because of no chlorophyll

12. 2. Sensor for Remote Sensing
Passive (energy leading to radiation received comes from an external source, e.g., the Sun; the Landsat is an example).
Active sensor (emits energy pulse, measure backscatter, records as a digital number e.g. LIDAR and RADAR).

13. LIDAR Remote Sensing
Light detection and ranging (lidar) mapping is an accepted method of generating precise and directly georeferenced spatial information about the shape and surface characteristics of the Earth.

14. Resolution of Satellite Sensor
Resolution is the smallest distance between two features, so that the two features can still be distinguished from each other. However, in remote sensing four types of resolution [28]:- 1- Spectral resolution 2- Spatial Resolution 3- Temporal Resolution 4- Radiometric Resolution

15. Spectral resolution
Refers to the dimension and number of specific wavelength interval in the electromagnetic spectrum to which a sensor is sensitive.
Many remote sensing systems record energy over several separate wavelength ranges at various spectral resolutions. These are referred to as multi-spectral, super-spectral, and hyper-spectral sensors

16. Spectral resolution

17. Spatial Resolution
This is a measure of the area or size of the smallest dimensions on the earth’s surface over which an independent measurement can be made by the sensor.
It is expressed by the size of the pixel on the ground in m
If a sensor has a spatial resolution of 20 meters and an image from that sensor is displayed at full resolution, each pixel represents an area of 20m x 20m on the ground.

18. Spatial Resolution

19. Radiometric Resolution
The radiometric resolution of an imaging system describes its ability to discriminate very slight differences in energy. i.e., it is a measure of how many grey levels are measured between pure black (no reflectance) to pure white.
It is measured in bits

20. Radiometric Resolution

21. Temporal resolution
Temporal resolution of a remote sensing system is a measure of how often data are obtained for the same area.
Applicable to satellite remote sensing only.
Importance of Temporal Resolution
Change in Land Use/ Land Cover
Temporal Variation
Monitoring of a Dynamic Event
Cyclone
Flood
Volcano
Earthquake

22. Landsat Launched in 1972, Managed by NASA and USGS
ETM 7+ has 7 bands (30 and 60 m) and a panchromatic (15)
Collected every 16 days.

23. LANDSAT
Landsat-1 through-7, systems have been included five different types of sensors there are:
Return Beam Vidicon(RBV) :
Multi-Spectral Scanner (MSS)
Thematic Mapper
Enhanced Thematic Mapper (ETM)
Enhanced Thematic Mapper Plus (ETM+)

24. LANDSAT8
Landsat 8 carries two instruments: The Operational Land Imager (OLI) sensor includes refined heritage bands, along with three new bands: a deep blue band for coastal/aerosol studies, a shortwave infrared band for cirrus detection*, and a Quality Assessment band. The Thermal Infrared Sensor (TIRS) provides two thermal bands. 
These sensors both provide improved signal-to-noise (SNR) radiometric performance quantized over a 12-bit dynamic range.
Improved signal to noise performance enable better characterization of land cover state and condition. Products are delivered as 16-bit images (scaled to 55,000 grey levels).

25. Spectral Bands LANDSAT7 &LANDSAT8

26. 3-Collect the data

27. Example/ College Station

28. College station Coverage
Path 26 Row 39

29. 4- Image processing
Preprocessing is concerned with correcting a degraded digital image to its intended form.
An image is a picture, photograph or any form of a two-dimensional representation of objects or a scene. The information in an image is presented in tones or colors.
A digital image is a two dimensional array of numbers. Each cell of a digital image is called a pixel and the number representing the brightness of the pixel is called a digital number (DN).

30. Correction Errors in remotely sensed data
Radiometric Correction: is removal of distortions in the amount electromagnetic energy received by the satellite, so that this energy received is the true reflected or emitted by the surface.
Radiometric corrections are made to the raw digital image data to correct for brightness values, of the object on the ground, that have been distorted because of sensor calibration or sensor malfunction problems.

31. Landsat 7 Scan Line Compensator Error L7 ETM+

32. Correction Errors in remotely sensed data
Geometric Correction ( registration/ rectification): are made to correct the inaccuracy between the location coordinates of the picture elements in the image data, and the actual location coordinates on the ground.
Several types of geometric corrections include system, precision, and terrain corrections.
The earth rotation, earth curvature, remotely sensed instruments are not constant . These effects and other effects are influenced on extracted image and cause geometric distortions in the image
RADIO/ This kind of correction is needed because of attenuation of energy before reaches the sensor, because of sensor irregularities such as striping scan line dropping and random noise [37].

33. Image Enhancement
The goal of image enhancement is to improve the detectable of objects or patterns in a digital image for visual interpretation [1]. image enhancement involves techniques for increasing the visual distinctions between features in a scene.
Contrast enhancement/ is required because digital data usually have brightness ranges that do not match the capabilities of the human visual system. There are two types of contrast enhancement [40].
linear contrast enhancement (linear stretch): Linear stretch converts the original digital values into a new distribution, using minimum and maximum values specified.
Nonlinear contrast enhancement(histogram equalization ): This method redistributes pixel values, so that there is approximately the same number of pixels with each value within a range.

35. Image Classification
classification process is to categorize all pixels in a digital image into one of several land cover classes or "themes" based on spectral-reflectance characteristics.
These categorized data may then be used to produce thematic maps of the land cover present in an image and/or produce summary statistics on the areas covered by each land cover type.
Two primary approaches can be used in image classification; Unsupervised classification and Supervised classification

36. Unsupervised Classification
Unsupervised classification involves algorithms that examine unknown pixels in an image and aggregate them into number of classes based on the natural groupings or clusters present in the image values.
The clustering algorithm is the statistical analysis of the sets of measurement pixels to detect their tendency to form clusters in multidimensional measurement space.
the user has to define the maximum number of clusters in a data set. Then, the computer locates arbitrary mean vectors as the center points of the clusters. Each pixel is then assigned to a cluster by the minimum distance between candidate pixel and each cluster mean. One of the simplest is (Euclidean distance) given by
this equation,

38. Supervised Classification
It can be defined as process of using samples of known identity to classify pixels of unknown identity. Samples of known identity are those pixels located within training area.
The analyst defines training areas by identifying regions on the image that can be clearly matched to areas of known identity on the image.
The analyst needs to know where to find the classes of interest in the area covered by the image. Supervised classification can be carried out by applying a classification algorithm after the training samples sets have been defined
Parallelepiped Classifier
Minimum Distance To Mean Classifier
Maximum Likelihood Classifier

39. Parallelepiped Classifier
Parallelepiped or box classifier based on the range of values in each category training set. This range may be defined by the highest and lowest digital number values or the mean and standard deviation in each band.

40. Vegetation indices
Vegetation index depend on the spectral reflectance of vegetation, which is very different in near-infrared and red bands.
Healthy vegetation should absorb the visible light and reflect most of the near-infrared; on the other hand unhealthy vegetation reflects more visible light and less near-infrared light.
Normalized Difference Vegetation Index (NDVI) is defined by the following general equation:
NDVI = 𝑁𝑒𝑎𝑟 𝐼𝑅 𝐵𝑎𝑛𝑑−𝑅𝑒𝑑 𝐵𝑎𝑛𝑑 𝑁𝑒𝑎𝑟 𝐼𝑅 𝐵𝑎𝑛𝑑+𝑅𝑒𝑑 𝐵𝑎𝑛𝑑
NDVI equation produces values in the range -1 (no vegetation) and +1 (high vegetation).

41. NDVI – Landsat ETM+

42. Surface Radiant Temperature
Thermal instruments operate at longer wavelengths. They are designed for detection of radiant temperature [44].
The radiant temperature emitted from the target (a given information about the targets) on the surface is measured by using thermal infrared band 6 (10.4 – 12.5μm) of Landsat 5 TM and Landsat 7 ETM+ images.
These information about targets usually scaled and stored as so-called digital numbers that rage from 0 to 255. The spectral radiance were converted into surface radiant temperature values by using the relationship[16]:

43. Example Land Surface Temperature For MODIS Satellite

44. Applications Land cover classification Land cover change detection
Global vegetation map
Water quality monitoring
Measurement of sea temperature
Snow survey
Monitoring of atmospheric constituents
Lineament extraction
Geological interpretation
Height measurement

45. References
Essential Image Processing and GIS for Remote Sensing, “Jian Guo Liu Philippa J. Mason Imperial College London, UK.
Application of remote sensing and geographical information system in civil engineering, Dr. Mohsin Siddique, Lecture.
Avery T.E., and Berlin G. L., (1992) "Fundamental of Remote Sensing and Airphoto interpretation", 5th ed., Prentice- Hall, Inc.
40. Campbell J.B., (1996)"Introduction to remote sensing", 2nd ed., Taylor and Francis, London.
Japan association of remote sensing ,(1996).
Mustafa N.H. , (2006), "Urban Growth for Baghdad city using Remote Sensing and GIS Techniques", M.SC., thesis, University of Technology, Iraq.