Image Information Extraction GEOG3610 Remote Sensing and Image Interpretation Image Information Extraction Image Information Extraction

Image Information Extraction GEOG3610 Remote Sensing and Image Interpretation Image Information Extraction Image arithmetic Vegetation indices Image classification supervised classification unsupervised classification Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Image arithmetic Image arithmetic uses mathematical operators on images in the similar way to those on mathematical variables. In general: U = f(A, B) e.g. Output = image1 + image2 The operation is on pixel-to-pixel basis without consideration of surrounding pixels. Image Information Extraction

Pixel-to-pixel operation GEOG3610 Remote Sensing and Image Interpretation Pixel-to-pixel operation Z A B U = f (A, B) Y U X Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Topographic effect The amount of illumination that a point on a surface receives is a function of the angle that the light is hitting the slope. Lambert’s cosine law: Image Information Extraction

Image showing topographic effect GEOG3610 Remote Sensing and Image Interpretation Image showing topographic effect Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Band ratio Sun DN on slope facing sun Unit Red NIR Red/NIR A 60 80 0.75 B 30 60 0.50 A B DN on slope facing away from sun A 45 60 0.75 B 20 40 0.50 Two stands of identical material will appear different if they are receiving differing amounts of illumination. The ratio between matching pixels in each band, however, will remain the same. Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation The use of band ratio Band ratios can be used in any instances where the absorption in one band is altered in some way. E.g. stressed/healthy vegetation algae in turbid water Image Information Extraction

Spectral band ratioing GEOG3610 Remote Sensing and Image Interpretation Spectral band ratioing TM Band 2 DN = 50 TM Band 3 DN = 30 TM Band 4 DN = 135 The New DN is computed by normalizing DNration in the range of [0, 255]. Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Vegetation indices Vegetation: absorption in visible and high reflectance in NIR. When the vegetation become stressed, absorption decreases in visible and increases in NIR. The higher the density of broad-leaf plants, the more distinct the difference between visible and NIR will be. Image Information Extraction

Spectral bands and signatures GEOG3610 Remote Sensing and Image Interpretation Spectral bands and signatures 0.4 0.6 0.8 1.0 2.0 2.6 1.2 1.4 1.6 1.8 2.2 2.4 20 40 60 Reflectance (%) Wavelength (m) Dry bare soil (grey-brown) Vegetation (green) Water (clear) SPOT VEG Landsat ETM+ SPOT HRG NOAA AVHRR Image Information Extraction

Computing vegetation index GEOG3610 Remote Sensing and Image Interpretation Computing vegetation index NIR band Red band VI image Image Information Extraction

Some common vegetation Indices GEOG3610 Remote Sensing and Image Interpretation Some common vegetation Indices Image Information Extraction

TM multispectral bands GEOG3610 Remote Sensing and Image Interpretation TM multispectral bands TM band 3 TM band 4 Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation VI images Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Image classification Image classification is the process of creating a meaningful digital thematic map from a image data set (information extraction). Supervised classification: classes from known cover types. Unsupervised classification: classes by algorithms that search the data for similar pixels. Image Information Extraction

Image classification process GEOG3610 Remote Sensing and Image Interpretation Image classification process What cover type is this? It has the spectral signature like wheat, the cover type is likely to be wheat. In this area, wheat is likely to be a farming land use. The thematic class is therefore “farmland”. Remotely sensed imagery Computer decisions Resulting class image Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Density slice Bmin Bmax Lmin Lmax Old pixel DN New pixel brightness Image Information Extraction

Supervised and unsupervised classification GEOG3610 Remote Sensing and Image Interpretation Supervised and unsupervised classification Image data Clustering Seed area (example pixels) Use clusters to define signatures or use clusters as classes Signature information Use a decision rule to class each pixel Thematic image ? Supervised classification Unsupervised classification Image Information Extraction

Supervised classification GEOG3610 Remote Sensing and Image Interpretation Supervised classification Training class selection (training areas/classes) Generating statistical parameters (spectral signatures) of training classes Data classification Evaluation and refinement Image Information Extraction

Training class selection GEOG3610 Remote Sensing and Image Interpretation Training class selection By selecting training area Water High buildings Concrete Bare soils Forest Image Information Extraction

Training class selection GEOG3610 Remote Sensing and Image Interpretation Training class selection By selecting spectral range TM Band 3 8 28 49 69 90 14 35 56 77 88 TM Band 4 Concrete Water Image Information Extraction

Supervised spectral classification GEOG3610 Remote Sensing and Image Interpretation Supervised spectral classification Band 1 DNs Band 2 DNs Crops Concrete Water U Band 1 DNs Band 2 DNs Crops Concrete Water U Image Information Extraction

Parallelepiped classifier GEOG3610 Remote Sensing and Image Interpretation Parallelepiped classifier 255 TM Band 3 TM Band 4 concrete high buildings grass slope water bare soils forest 255 TM Band 3 TM Band 4 concrete high buildings grass slope water bare soils forest 1 2 Image Information Extraction

Minimum distance classifier GEOG3610 Remote Sensing and Image Interpretation Minimum distance classifier 255 TM Band 3 TM Band 4 concrete high buildings grass slope water bare soils forest 255 TM Band 3 TM Band 4 concrete high buildings grass slope water bare soils forest 1 2 Image Information Extraction

Maximum likelihood classifier GEOG3610 Remote Sensing and Image Interpretation Maximum likelihood classifier Band x Band y Samples Image Information Extraction

Maximum likelihood classifier GEOG3610 Remote Sensing and Image Interpretation Maximum likelihood classifier 255 TM Band 3 TM Band 4 concrete high buildings grass slope water bare soils forest 255 TM Band 3 TM Band 4 concrete high buildings grass slope water bare soils forest 1 2 Image Information Extraction

Unsupervised classification GEOG3610 Remote Sensing and Image Interpretation Unsupervised classification Cluster size Distance between cluster means Distance to a cluster mean 255 Band A Band B Clustering: use predefined parameters to identify cluster locations in data space and then to determine whether individual pixels are in those clusters or not. Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Clustering Band 1 DNs Band 1 DNs Cluster 1 Cluster 2 Cluster 3 Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Clustering process 255 Band A Band B A 255 Band A Band B B A) Cluster centres are arbitrarily assigned B) Each pixel is assigned to the nearest cluster centre in data space Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Clustering process 255 Band A Band B C 255 Band A Band B D C) The cluster means are then calculated and the pixels are reassigned to the new cluster centres D) The process is repeated until when the cluster centres move by less than a preset distance Image Information Extraction

Classification procedures GEOG3610 Remote Sensing and Image Interpretation Classification procedures A No Separate data into groups with clustering Classify data into groups Assign name to each group Satisfactory? Yes No Form images of data Choose training pixels for each category Calculate statistical descriptions Satisfactory? Yes B Classify data into categories defined Flow diagrams representing unsupervised classification (A) and supervised classification (B). Image Information Extraction

Post-classification sorting GEOG3610 Remote Sensing and Image Interpretation Post-classification sorting Purpose: "smooth" the classified image to make it look closing to a thematic map Processes: "Sieve" to remove isolated pixels "Clump" to aggregate the sparsely distributed pixels into "clumps" (larger areas) Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation ‘Cleaned’ images Water Concrete High buildings Bare soils Grass slope Forest Image Information Extraction

GEOG3610 Remote Sensing and Image Interpretation Summary Information extraction is the key process to convert image data to spatial information. Vegetation index (VI) uses the significant difference between red and near-infrared bands shown on the vegetation spectral signatures. VI is a quantitative measurement but its value is only in relevant rather than absolute terms, and has no physical meanings. Classification is used to identify and separate pixel clusters in the spectral space. Common approaches are supervised and unsupervised classifications. Image Information Extraction