Presentation on theme: "Spectral Angel Mapper (SAM) Algorithm for Landuse Mapping"— Presentation transcript:
1 Spectral Angel Mapper (SAM) Algorithm for Landuse Mapping Partha Pratim GhoshProduct Specialist, ESRI IndiaDr. Deb Jyoti PalVice President, ESRI IndiaDr. Pabitra BanikProfessor, Indian Statistical Institute, KolkataDr. Nilanchal PatelProfessor & Head, Birla Institute of Technology, Ranchi
2 Agenda Hypothesis Research Question Advantages of Spectral Angel Mapper (SAM)Minimum Noise Fraction (MNF)Pixel Purity Index (PPI)n-Dimensional Visualizer (n-D)Endmember CollectionClassificationResultConclusion
3 HypothesisLand use and land management practices have a major impact on natural resources including water, soil, nutrients, plants and animals. Accurate Land use information must be develop for accurate policy making.Digital Image classification is one of the well accepted method to extract Land use information system and many limitation like mixed pixel and noise issues has been observed in the conventional pixel based classification techniques.
4 Research QuestionCan we use spectra based classification techniques for multispectral image to develop an accurate land use information system?Can we overcome the issues related to mixed pixel specifically observed in case of different type of vegetation?Can we identify crops using spectra?
5 Eastern Part of Eastern Plateau Area (Purulia District of West Bengal) Study AreaEastern Part of Eastern Plateau Area(Purulia District of West Bengal)ImageLandsat ETM+ ImagePurulia District, West BengalIndia
6 LandcoverLand cover is the physical material at the surface of the earth, which naturally cover the earth surface.e.g. grass, asphalt, trees, bare ground, water, etc..
7 Landuse“The arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it is called land use" (FAO, 1997a; FAO/UNEP, 1999).Land use involves the management and modification of natural environment or wilderness into built environment such as fields, pastures, and settlements.
8 Landcover & Landuse (LULC) Map When only land cover represented through map is known as Land Cover mapWhen only land use represented through map is known as Land Use mapWhen both land cover and land uses are represented through a single map is known as Land Cover / Land Use (LULC) map.e.g. Grass is land cover; pasture and recreational parks are land uses of grassRef: The origins of the ‘land cover / land use’ couplet and the implications of their confusion are discussed in Fisher et al. (2005).
10 Limitation of Pixel Based ClassificationEach pixel in the image is compared to the training site signatures identified by the analyst and labeled as the class it most closely "resembles" digitally.Class 1VegetationUrbanWaterClass 2UrbanVegetationClass 3Water
11 Advantage of SAM Classification SAM measures the spectral similarity by calculating the angle between image spectrums to reference reflectance spectra, treating them as vectors in n-dimensional space (Kruse et al., 1993; Van der Meer et al., 1997; Rowan and Mars., 2003).Small angles between the two spectrums indicate high similarity and high angles indicate low similarity.SAM is different from conventional classification methods because it compares each pixel in the image with every endmember for each class and assigns a ponderation value between 0 (low resemblance) and 1 (high resemblance).
12 Spectral Angel Mapper (SAM) SAM is an automated method for comparing image spectra to individual spectra or to a spectral library (Boardman, unpublished data; CSES, 1992; Kruse et al., 1993a).Image SpectraLaboratory SpectraImage Spectra & Laboratory Spectra are matchingThe algorithm determines the similarity between two spectra by calculating the spectral angle between them, treating them as vectors in n-D space, where n is the number of bands.
13 Spectral Angel Mapper (SAM) In a two-dimensional feature space defined by bands x and y, two spectral signatures that represent two different surface objects can be represented as vectors v1, and v2. Then the spectral distance (Euclidean distance) is the length of the line segment d connecting the end points of the two vectors v1 and v2. The spectral angle is the angle between the two vectors v1 , and v2 : i.e.,θ (v1, v2)=Cos -1v1Tv2v1 v2
14 Spectral Angel Mapper (SAM) If we linearly scale the length of vectors v1 and v2, by distance r, the spectral distance will be scaled by r.On the other hand the cosine of the angle θ between the two vectors v1 and v2, remains the same.Because of this invariant nature of the cosine of the angle θ to the linearly scaled variations, it becomes sensitive to the shape of the spectral patterns. Sohn et al. (1999)
15 Spectral Angel Mapper (SAM) Small spectral angel (Cos θ) between the two spectrums indicate high similarity and high angles indicate low similarity.The spectra of the same type of surface objects are approximately linearly scaled variations of one another due to the atmospheric and topographic variations. So the actual vectors in feature space will fall slightly above or below the linearly scaled vectors. But the changes in the cosine of the angle θ caused by these variations remain very small (Sohn et al., 1999).
16 Method Atmospheric Correction of Image Minimum Noise Fraction (MNF) Pixel Purity Index (PPI)n-Dimensional Visualizer (n-D)Endmember CollectionCreation of Unidentified Spectral LibraryClassification using SAMClass Identification
17 Minimum Noise Fraction (MNF) MNF transform is used to segregate noise in the data, and to reduce the computational requirements for subsequent processing (Boardman and Kruse, 1994). The MNF transform as modified from Green et al. (1988) and used in ENVI
18 Pixel Purity Index (PPI) The Pixel Purity Index (PPI) is a means of finding the most “spectrally pure,” or extreme, pixels in multispectral and hyperspectral images (Boardman et al., 1995).The Pixel Purity Index records the total number of times each pixel is marked as extreme. A "Pixel Purity Image" is created in which the DN of each pixel corresponds to the number of times that pixel was recorded as extreme.
19 n-Dimensional Visualizer Spectra can be thought of as points in an n -dimensional scatter plot, where n is the number of bands.The n-D Visualizer help to visualize the shape of a data cloud that results from plotting image data in spectral space (with image bands as plot axes).We typically used the n-D Visualizer with spatially subsetted Minimum Noise Fraction (MNF) data that use only the purest pixels determined from the Pixel Purity Index (PPI). .
20 n-Dimensional Visualizer Rotating n-D Visualizer interactively we can select groups of pixels in classes. Selected classes can be exported to us in the classification.n-D Visualizer can be used to check the separability of the classes when the regions of interest (ROIs) as input into supervised .The n-D Visualizer is an interactive tool to use for selecting the endmembers in n-D space.
21 Endmember CollectionEndmembers are spectra that are chosen to represent pure surface materials in a spectral image. Endmembers that represent radiance or reflectance spectra must satisfy a positivity constraint (containing no values less than zero).
22 Use the Endmember in Spectral Angel Mapper Algorithm SAM ClassificationUse the Endmember in Spectral Angel Mapper Algorithm
23 Class Identification Surveyed villages and markets in Purulia District District marketMajor marketsMinor marketsRailwayRoadBlock BoundarySurveyed villagesLegend:Data source: ISI, Calcutta, India
24 Landuse Ecosystem Pattern of Kashipur Block Class IdentificationLanduse Ecosystem Pattern of Kashipur Block
26 ConclusionEven if Landsat ETM+ is a medium spatial resolution and that sub-pixel contamination cover material is evident while selecting endmembers, it has given good results in SAM.The classification map generated with SAM for Landsat ETM+ show that this method could effectively be used for landuse mapping.With the help of MNF, PPI & n-D Visualizer the mixed pixel issue can be addressed up to certain level
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