Extraction of Blue Ice Area in Antarctica

Extraction of begrimed Ice Area in polaraChapter 3METHODOLOGYHigh contract satellite selective information has made it affirmable to obtain optimistic results in feature downslope processes. High resolution World-View-2 entropy is use for mapping dark scratch aras (BIAs) in south-polar pieces. World-View-2 provides extensively high accuracy, agility, capacity and ghostly diversity. First high-resolution 8-band multispectral commercial/business satellite is World-View-2 launched October 2009. Working at an elevation of 770 kilometres, World-View-2 gives 46cm panchromatic resolution and 1.85m multispectral resolution. World-View-2 has a normal revisit time of 1.1 days and it is able of catching up to 1 million squargon kilometres of 8-band bodry per day. Satellite pictures gener anyy atomic number 82 seasonal annual variations in BIAs coverage over the outgoing 30 year on the East Antarctic plateau region. In late studies, the distri thation of BIAs cigaretister likewis e mulled over from the SAR (synthetic aperture radar) take tos. In SAR satellite pattern, meritless frosting can likewise be externally perceived. The amplitude of savory field glass is less than that of speed of light (white), because the frost fold is smoother than the latter. Yet, distinction is not at all that conspicuous when applying Semi-automatic stemma approach. Blue ice can be distinguished effortlessly in the coherence map got from two SAR pictures in a sketch of higher(prenominal) coherence of blue ice. It is additionally found that the picture texture data is useful for distinguishing various types of blue ice like rough, smooth and take aim blue ice. In this study, Atmospheric corrected (QUAC) sharpen calibrated image (World-View-2 data) is used for chicken outing blue ice field of thoughts in Schirmacher Oasis in Antarctic region. Extraction of blue ice sweep in Antarctica deal with the entireness area of blue ice areas excluding the other feature ( non-tar arse about) appearing on or near it. Blue ice areas have some unique(predicate) qualities that make them of special interest for origin as they are precisely 1% of Antarctic region. Many remote sensing approaches have been enforced to monitor and map Antarctic BIAs.3.1 Methodology ProtocolThe beginning of blue ice areas is simplified by the Methodology protocol. As the whole image takes time for processing, as Schirmacher Oasis is with an area of 34km, ranks among the smallestAntarctic oasis and is a typicalpolar desert, so the image is divided in 12 test tiles of antithetic separate of entire World-View-2 image to achieve prior results. Atmospheric correction is do with QUAC (quick atmospheric correction) method to obtain better results. Atmospheric correction to each tile added suitable outputs results to workflow. Calibrated data is also used without applying atmospheric correction to it. Multiband image combination was made from atmospheric corrected data and cali brated data of the study area.Alternating snow and blue ice bands surface patterns are generally found in East Antarctica due(p) to which it is hard task to clearly extract BIAs. For feature declension processes region of interest (ROI) is considered in which blue ice is bulls eye and white ice appearing on or near the blue ice is considered as non- aim. Methodology workflow is inclined(p) in order to achieve right and prior results comparing with the previous studies. Extraction of blue ice is not that easy task as dust and white snow appears on it as non-target. Various Semi-automatic extraction methods like TERCAT, objective lens Detection Wizard, interpret Methods, unearthly Matching and Object Base watch Analysis (OBIA) are used for extracting blue ice areas in Antarctica. The initial results obtained were good but not better enough to keep them prior. Many trials were carried out for extracting blue ice in Antarctica. Prior results were kept in workflow of methodologi cal analysis to compare them with every trial results.Object ground and Pixel based both the classification are used in workflow to get good results. From the High resolution World-View-2 data reference data (digitized data) was prepared for blue ice area and extracted blue ice area was obtained from Semi-automatic extraction methods and OBIA. From the extracted blue ice, blue ice is considered as target and white snow appearing on it as non-target. Comparing reference data and extracted data Bias, % Bias and RMSE values were calculated. After that Average for Bias, % Bias and RMSE values is estimated. preconception= % BIAS= RMSE= Where,Ref A is Reference area and Ext A is Extracted arean= no. of tiles processed.3.2 Semi-automatic extraction methodsThe semi-automatic feature extraction approach intuitively makes endeavours to commonly empowering the insight or data of human perception good example to robustly detect the targeted feature and the computer-aided system to bring fast extraction of targeted feature and exact shape representation. In semiautomatic feature extraction strategy, first target feature is detected by human fantasy and a couple of estimates in terms of seed points or coaching samples concerning the targeted feature on highlight are typically given. The targeted feature is thusly portrayed automatically by the PC helped calculations.3.2.1 TERCAT approach (ENVI 5.1 Exellis Help) 33The Terrain Categorization (TERCAT) peckerwood creates an output product in which pixels with same spectral properties are clumped into categories. These categories whitethorn be either user-defined, or automatically generated by the classification algorithm. The TERCAT beam of light provides all of the standard ENVI classification algorithms, plus an additional algorithm called master Takes All. This is a voting method that classifies pixels based on the majority compiled from all of the other classification methods that were conducted. In this research, the sub approaches for TERCAT are Maximum Likelihood, Spectral Angle Mapper, Parallelepiped and Winner Takes All.3.2.2 designate Detection approach (ENVI 5.1 Exellis Help) 33Target detection algorithms work on the principle of extracting target features based on spectral characteristic of initial coaching spectral signatures of target features, and effecting end to the background noise using spectral signatures of non-target features. If the users knows that the image contains at least one target of interest, the wizard can be used to find other targets like it in the same image. The workflow can also be accessed programmatically, so the user can customize options if needed. Target detection tools (ENVI 5.1) were executed to perform supervised image processing tasks into workflows (CEM, ACE, OSP, TCIMF, and MT-TCIMF) to extract blue ice areas (BIAs) as target and white ice as non-target.3.2.3 Spectral Matching approach (ENVI 5.1 Exellis Help) 33Spectral matching approaches extract the target features that are described in multispectral mental imagery based on the target features spectral characteristics. Spectral matching algorithms confirm the spectral similarity or matching between input satellite imagery and reference key points to form an output product within which pixels with similar spectral properties are clumped into target and non-target categories. Spectral Matching (ENVI 5.1) were executed to perform supervised image processing tasks into workflows (MF, SAM, MTMF and SAMBM) to extract blue ice areas (BIAs) as target and white ice as non-target.3.2.4 mathematical function Methods approach (ENVI 5.1 Exellis Help) 33Selected hyperspectral Mapping Methods describes pass on concepts and procedures for analyzing imaging spectrometer data or hyperspectral images. Spectral selective information Divergence (SID) is a spectral classification method that uses a going away measure to match pixels to reference spectra. The smaller the divergence, the mor e likely the pixels are similar. Pixels with a measurement greater than the specified maximum divergence doorsill are not classified. End member spectra used by SID can come from ASCII files or spectral libraries, or you can extract them outright from an image (as ROI average spectra). Mapping Methods (ENVI 5.1) were executed to perform supervised image processing tasks into workflows SID SV (0.05), SID SV (0.07), SID SV (0.1), SID MV (0.05) and SID MV (0.09) to extract blue ice areas (BIAs) as target and white ice as non-target.3.2.5 Object Based Image Analysis (OBIA) approach (Ecognition Developer Help) 34Object Based Image Analysis (OBIA), is an advanced method used to segment a pixel based image into map objects that can then be classified as a whole. This kind of analysis is ideal for mapping with high-resolution imagery, where a oneness feature (such as a tree) might have several different shades of pixels. The example of rule-set for audition 1, 2, 3 and 4 for extracting blue ice areas in this research is stated belowFor Trial 102.063 50 shape. 0.8 compact.0.6 Creating level 1 exporting view to partitioning (no geo)Unclassified with specify nir-1=50 and nasty nir-1Export view to peg down class (no geo)Blue ice with mean nir-1=50 and mean nir-1Export view to unite (non geo)For Trial 202.063 60 shape. 0.8 compact.0.6 Creating level 1Export view to segmentation (no geo)Unclassified with mean nir-1=100 and mean nir-1Export view to assign class (no geo)Blue ice with mean nir-1=100 and mean nir-1Export view to merging (non geo)For Trial 302.063 70 shape. 0.8 compact.0.6 Creating level 1Export view to segmentation (no geo)Unclassified with mean nir-1= one hundred fifty and mean nir-1Export view to assign class (no geo)Blue ice with mean nir-1=150 and mean nir-1Export view to merging (non geo)For Trial 402.063 80 shape. 0.8 compact.0.6 Creating level 1Export view to segmentation (no geo)Unclassified with mean nir-1=200 and mean nir-1Export view to assi gn class (no geo)Blue ice with mean nir-1=200 and mean nir-1Export view to merging (non geo)The on-top rule-set is employed to extract blue ice areas as well as non-target depending on their mean band values. OBIA is making bulky progress towards spatially explicit information extraction advancement, such as is required for spatial planning as well as for umpteen monitoring programmes.The Semi-automatic extraction strategies and OBIA utilized in this study to extract blue ice areas (BIAs) are supported differently on different profound principles. To compare these strategies objectively, we kept the input ROIs (regions of interest or coaching samples) perpetual for all methods for each tile. ROIs are different for different tiles as the area differs. After classifying the image into target spectra, i.e., blue ice areas, using the Semi-automatic extraction methods and OBIA approaches, the 12 semi-automatically extracted tiles (for BIAs) were vectorized to calculate the area of in dividual tile.