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HYPERSPECTRAL REMOTE SENSINGECV 5511PREPARED BY:FAHMI NASRULLAHNUR HIDAYAH BT ISHAK HIZAMSUZALINA BT KAMARUDDINCLASSIFICATION OF AVIRIS DATA FOR CROPS MAPPING USING HYPERSPECTRAL APPROACH PRESENTATION OUTLINEThis presentation consists of:a) Introductionb) Material and method - Study area - Satellite and ancillary data - Digital image processingc) Accuracy assessmentd) ConclusionINTRODUCTIONINTRODUCTIONHyperspectral remote sensing is one of technique for acquiring image but differs from multispectral imagingMultispectral imaging produce images with few relatively broad wavelength bands while hyperspectral imaging, collect image data simultaneously in dozens or hundreds of narrow, adjacent spectral bands. Images produced from hyperspectral sensors contain more data to be interpreted compared to multispectral sensors. Example: Multispectral image can be used to map agriculture area while hyperspectral image can be used to map agriculture species within the agriculture area.

INTRODUCTIONIn vegetation studies, hyperspectral data can be used to characterize, model, classify and map agricultural crops and natural vegetation specifically in study of species composition, vegetation crop type, biophysical properties, biochemical properties, disease and stress, nutrients, moisture and others ( Thenkabail et al.2000)Classification of surface features in satellite imagery is one of the most important applications of remote sensing. There are two types of classification which unsupervised and supervised methods.

Thenkabail P.S., Smith, R.B., and De-Pauw, E. (2000) Hyperspectral vegetation indices for determining agricultural crop characteristics. Remote sensing of Environment. 71:158-182.INTRODUCTIONHowever, the main problem with supervised methods is that the learning process depends heavily on the quality of the training data set and the input space dimensionality.While, unsupervised method is not sensitive to the number of labelled samples since they work on the whole image.Traditionally, agricultural crops are identified using broadband satellite imagery by the classification of satellite imagery with statistical classifiers such as the Maximum Likelihood (ML) classifier (Philipp and Rath, 2002).

Philipp I, Rath T (2002) Improving plant discrimination in image processing by use of different colour space transformations. Comput Electron Agric 35(1):115INTRODUCTIONRecent advances in sensor technology have led to the development of hyperspectral remote sensing imaging devices which can obtain high-spectral resolution radiance data for each location (pixel) within the field of view (Chen et al. 1999).It is expected that such detailed spectral data will permit the unique identification of most surface types of rocks, soils and vegetation, provided that the spatial resolution of the data is sufficient to represent a single surface type for each spectrum.

Chen JM, Leblanc SG, Miller JR, Freemantle J, Loechel SE,Walthall CL, Innanen KA, White HP (1999) Compact airborne spectrographic imager (CASI) used for mapping biophysical parameters of boreal forests. J Geophys Res 104:2794527958MATERIAL & METHODSTUDY AREAThe Flevopolder (52 30 N and 5 28 E) with total area of 970 km2 located in Flevoland, Netherlands.

SATELLITE DATAOn 5 July 1991 AVIRIS acquired data over the Flevoland polder.AVIRIS records data in 224 spectral bands of 10 nm width. The flight height is 20 km and the size of the pixels is approximately 20 m 20 m. The 224 spectral bands cover the spectral range of 400 - 2400 nm. Data are available as unsigned 16-bit data.

ANCILLARY DATAAgricultural land use in the Flevopolder was surveyed during the AVIRIS overflight and comprised of potatoes, sugar beets, wheat, maize, peas, beans, flax, onions and grass. This is the location of different farms and agricultural fields in the Flevopolder where the numbers refer to the different farms and farms owners in 1991.

Beans

Flax

Grass

Maize

Onions

Potato

Sugar beet

Wheat

CloverANCILLARY DATAThis is parcel division of different agricultural fields and the key to the crops.

METHODOLOGYDATA PREPARATIONPRE PROCESSINGRadiometric correctionConvert DN to reflectanceHyperspectral AnalysisMinimum Noise FractionPixel Purity IndexN D VisualizationEndmember ExtractionSpectral Angle MapperDATA CLASSIFICATIONACCURACY ASSESSMENTMaximum LikelihoodSelect training areaSupervised classification14PRE - PROCESSINGThe pre processing involved two main stages:a) Radiometric correctionb) Convert DN to reflectance(a)Radiometric correctionThe pre - processing, consisting of dark current correction, vignetting and radiometric calibration of the sensors themselves, is performed by the Jet Propulsion Laboratory (JPL) in Pasadena, California.

PRE - PROCESSING(b)Convert DN to reflectanceThe observed digital numbers (DNs) are linearly related to radiances (in mW/cm2/m/sr) according:

Radiance = DN / 200

Reflectance can be calculated using formula:

Reflectance = offset + gain radiance

where offset and gain for the calibration of AVIRIS has been given.

DATA CLASSIFICATIONThe data classifications were performed using classifications approaches:a) Maximum Likelihoodb) Hyperspectral Analysis ApproachMaximum Likelihood Two steps were carried out in the extraction of crop types:i) Select training areaii) Supervised classificationHyperspectral Analysis ApproachFour steps were carried out in the extraction of crop types:i) Minimum Noise Fractionii) Pixel Purity Indexiii) N D Visualization and Endmember Extractioniv) Spectral Angle Mapper

Maximum LikelihoodMaximum Likelihood ClassifierMaximum Likelihood classifier was employed as one of the common widely used classifier to classify the satellite data.It served as comparison to hyperspectral approach examined in this study.The classification was performed using supervised approach where all the three bands are the main feature input using designated training area.Hyperspectral Analysis(i) Minimum Noise FractionThe MNF transform was applied to further segregate noise thus found in the data (Green et al. 1988).

MNF Transformation BandsEigenvalues of MNF Transformed Bands versus band number

(i) Minimum Noise Fraction

MNF Band 3 2 1MNF Band 7 4 2(ii) Pixel Purity IndexWithin the feature space, the noise free data occurred as a continuous class from the purest to variety of mixtures. The purest was referred to endmembers while mixtures were considered as sub compositions due to pixel attributes.

Pixel purity index imagePixel purity index plot(ii) Pixel Purity Index

PPI image enhanced using density sliceImage showing pure or extreme pixel

(iii) n - D Visualization and Endmember ExtractionThe distribution of the AVIRIS data in n space was used to estimate the number of spectral endmembers and their pure spectral signatures and to help understand the spectral characteristics of the material which make up the signature.Image generated from PPI was used as the input in this session. Spectral library for crop types of study area was built using ROI export from n D visualization.Different classes generated from n D Visualization were compared to spectral library to identify each class. (iii) n - D Visualization and Endmember Extraction

The n dimensional visualizer and spectral class for each endmembers

(iv) Classification using Spectral Angle Mapper (SAM)The SAM is an automated method for comparing image spectra to individual spectra or spectral library (Kruse et al., 1993).It assumes that the data to be classified have been reduced to apparent reflectance.A simplified explanation of this can be given by considering a reference spectrum and an unknown spectrum from two - band data.RESULTSThe classification result of AVIRIS image using (a) Maximum Likelihood (b) Spectral Angle Mapper is shown in the figure below:a)

b)

ACCURACY ASSESSMENTACCURACY ASSESSMENTMaximum LikelihoodAccuracy = 60.57 %Kappa coefficient = 0.52ClassProducers Accuracy (%)User Accuracy (%)Water100100Wheat77.69100Onion76.195.23MaizeSugar beet00Clover00Bean51.2179.70Potato10084.19Grass66.2921.22Peas00ACCURACY ASSESSMENTb) Spectral Angle MapperAccuracy = 47.59 %Kappa coefficient = 0.37ClassProducers Accuracy (%)User Accuracy (%)Water1.45100Wheat61.26100Onion255.23Maize00Sugar beet21.7029.68Clover00Bean50.9748.84Potato75.8384.72Grass1.120.31Peas11.633.94CONCLUSIONTHANK YOU