Summary
Summary Midterm review notes
- Course
- GEOG 371
- Institution
- University Of Waterloo (UW )
Good to have for GEOG 371 midterm review
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1. False Colour Composite (FCC) means image bands are not being assigned to their correspondedcolour channel. For example, near-infrared band is assigned to red channel; red band is assigned
to green channel, and green band is assigned to blue channel. FCC is powerful because it could
outline land features much clearer than True Colour Composite (TCC). We can easily distinguish
different land covers in an image through our eyes when we use FCC since the colour contrast is
obvious between different land features. For instance, since green vegetation has a high spectral
response in near-infrared region of the spectrum, vegetation appears red in the image when
near-infrared band is assigned to red channel. Since water has low spectral response in many
parts of the spectrum, water appears black in many FCC.
2. This is because our remote sensing technology advances over the past couple of years. Before if
we want to analyze daily cloud coverage, we cannot use high spatial resolution image to make
analysis since we need to use high temporal resolution data to make this analysis, but there
wasn’t any high spatial resolution image available in this scenario. However, we can easily
analyze daily cloud coverage currently since we can achieve both high spatial and temporal
resolution simultaneously. For monitoring snow cover melting for every 2 to 3 days, Landsat is
not a good choice since its temporal resolution is every 16 days. Modis is a good option in this
case since its temporal resolution is much higher than Landsat.
3. This is because we want to remove the noise as much as possible so that we can make further
analysis using the image easily. The noise is made of wave that is from atmospheric scattering
effect and reflectance from an area that we are not interested in. Therefore, removing those
noises is critical since we only want to analyze any reflectance from our area of interest.
Radiometric correction can correct for noises that is from atmospheric scattering effects and
reflectance from neighboring area. It cannot correct for total cloud coverage for visible and
infrared. However, microwave remote sensing can help with this since microwave can penetrate
through clouds.
Object-based image classification means to classify an image based on objects whereas each
individual pixel of the image is classified for per-pixel classification. Each object is a group of
pixels with similar spectral and spatial characteristics. The way that the object is defined is
varied. It depends on the algorithm that a user uses to create objects for classification purposes.
Once the objects in the image are established, the classification process is similar to per-pixel
classification. For object-based classification, all pixels within the same object is classified to a
unique land cover type. Since object-based classification considers spatial correlation between
features in the image besides spectral characteristics of the features, it would have higher
classification accuracy for object-based image classification in general than for per-pixel
classification especially for high spatial resolution image. For example, some parts of urban
residential area most likely will be classified as forest in per-pixel classification since there are
trees in residential area. However, the chance of this situation to happen in object-based
classification is slim. Most likely, all residential area will be classified as residential area in object-
based classification.
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