ERDAS IMAGINE – A Comprehensive GIS Solution

What is ERDAS IMAGINE?

Geographic imaging professionals use specialized software in processing of a huge quantity of geospatial data on a daily basis. This software is designed for a specific roles as well as add-on applications through creation of the same number of problems as they can solve. Producer Suite of Power Portfolio normally offers the ERDAS IMAGINE. Producer Suite interface enables the user to gather information, process it, analyze as well as understanding the collected geospatial data (Ruiz et al., 2019). Finally, the collected data is disseminated as useful information in the form of Hexagon Geospatial GIS linked with desktop, remote sensing as well as in the form of photogrammetry offerings.

Through the use of ERDAS IMAGINE it is possible to attain the true value, ability to consolidate remote sensing, photogrammetry, LiDAR analysis, and analysis of basic vectors as well as processing of radar into a single unit.  ERDAS IMAGINE usually offers several solutions in one package including incorporation of standards, capabilities of the enterprise as well as products in the following ways:

• Analysis of images, remote sensing as well as Geospatial Imaging System
• Offers support for optical multispectral, hyper spectral as well as panchromatic imagery, radar and data on LiDAR
• Provision of a ribbon interface that is user-friendly
• Processing that is distributed and has multiple cores
• Provision of spatial modeling technique having a raster, vector as well as point cloud operators. In addition, it enables real-time preview of results (Chavan et al., 2021)
• Terrain operation with high performance as well as mosaicking
• It offers several tools for detecting any changes encountered
• Mapper algorithm support system on ERDAS ER
• Capacity of converting greater than one hundred and ninety images formats to major file formats. Major file formats include: NITF, Geo TIFF, CARDRG, JPEG2000, ECW as well as MrSID.
• Provision of comprehensive OGC internet services which include Web Processing Services, Web Coverage Services, Web Mapping Services as well as Catalog Services

Some of the primary sources data used in mapping as well as management of features or resources include imagery and LiDAR. It has been established that geospatial data archives enables one to carry out referencing as well as measuring the quantity of change encountered in a given geographical area in such activities as studying the changes in urban population growth, sensitive surroundings, resources for mapping or assessment of damages caused by natural calamities (Xi et al., 2020). In order to obtain a quicker as well as make more informed decisions, it is important that accurate and updated data is used. It has also been established that ERDAS IMAGINE has the capacity of uniting professionals located in different departments within the same organization thereby saving on the amount of time need on production while improving productivity. This particular intuitive single interface enables co-workers, traders as well as clients to work on the same data and all of them obtaining consistent results in the project being undertaken. It is also possible to customize ERDAS IMAGINE thereby simplifying workflow within the organization.

• Enables discovery of the potential of the existent imagery

ERDAS IMAGINE has been established to able to simplify classification of images as well as segmentation, ortho-rectification, mosaicking, reprojection, elevation extraction as well as image interpretation.

• Multiple Classification Solutions

ERDASIMAGINE has been established to provide K-means, ISODATA, segmentation of images based on objects, Machine Learning as well as Deep Learning Artificial Intelligence algorithms.

• Enables streamlining of workflow as well as saving on time

It has been established that very powerful algorithms together with data processing functionalities are tirelessly operating behind the scenes thereby enabling strict concentration on the analysis processes

• The capacity of Spatial Modeler

The graphical editing environment of a spatial modeler offers flexibility in capturing domain expertise while converting the output into re-usable algorithms.

• It allows for Point Cloud Processing

ERDAS IMAGINE exclusively provides an interface for display, editing as well as analysis of point clouds that are derived from LiDAR or point correlation stereo pairs.

ERDAS IMAGINE Solutions

Multispectral type is the manner of sorting pixels right into a finite quantity of man or woman lessons, or categories of facts, based totally on their facts document values. If a pixel satisfies a positive set of criteria, the pixel is assigned to elegance that corresponds to that criteria. This method is likewise called image segmentation. Depending on the type of information you want to extract from the authentic facts, instructions may be related to recognised functions on the floor or may additionally actually represent areas that look exceptional to pc (Atoma et al., 2020).

An instance of a categorized picture is a land cover map, showing plants, naked land, pasture, city, etc. The class process breaks down into two components: education and classifying (the usage of a decision rule). Training is necessary since the pc gadget have to learn to recognize styles within the facts. Training is the process of defining the criteria with the aid of which those patterns are identified. Training may be carried out with both a supervised and an unsupervised approach. Supervised schooling is carefully managed with the aid of analyst. In this process, you select pixels that represent styles or land cover functions which you understand, or that you can pick out with help from other resources, along with aerial pix, floor truth information, or maps.

Knowledge of information, and of classes desired, is needed earlier than classification. By identifying styles, you could instruct the computer gadget to become aware of pixels with comparable traits. If the classification is accurate, the ensuing training represent the kinds within the facts that you originally diagnosed. Unsupervised schooling is extra pc-computerized. It enables you to specify some parameters that the laptop makes use of to find statistical patterns which are inherent within the records. These patterns do now not always correspond to directly significant traits of scene, consisting of contiguous, effortlessly recognized areas of a selected soil kind or land use (Twumasi et al., 2020).

They are truly clusters of pixels with comparable spectral traits. In some cases, it is able to be greater essential to discover businesses of pixels with similar spectral characteristics than it is to type pixels into recognizable categories. The end result of schooling is a hard and fast of signatures that defines a education sample or cluster. Each signature corresponds to a category, and is used with a choice rule (explained under) to assign the pixels within the picture document to a category. After the signatures are defined, the pixels of image are looked after into instructions based on the signatures with the aid of use of a classification decision rule. The choice rule is a mathematical algorithm that, using data contained within the signature, performs the actual sorting of pixels into wonderful magnificence values.

In supervised education, it is essential to have a fixed of desired classes in thoughts, and then create the ideal signatures from the information. You have to additionally have a few way of spotting pixels that represent the classes which you want to extract. Supervised type is normally appropriate when you want to identify notably few lessons, if you have decided on education websites that may be verified with ground fact information, or when you can become aware of distinct, homogeneous regions that represent every magnificence. On the alternative hand, in case you need the training to be decided by using spectral distinctions which might be inherent in the statistics so that you can define the lessons later, then the utility is higher proper to unsupervised schooling (Twumasi et al., 2020). Unsupervised schooling enables you to define many lessons easily, and pick out training that aren't in contiguous, without problems recognized regions.

Benefits of ERDAS IMAGINE

Many reports approximately modelling land cover sorts and plant life coverage use guide of Landsat products and far off sensing methods. Though the style ofm focus on different studies regions in Mediterranean vicinity, they were reviewed on this study and their developed methodologies and techniques have been taken into consideration. The Landsat Thematic Mapper (TM) snap shots were downloaded from the Global Land Cover Facility (GLCF) Earth Science Data Interface internet site (https://glcfapp.Glcf.Umd.Edu). For choosing the goal location, a spatial mask of coordinates ranging from 26°00’–26°00’ E to 38°00’–39°00’N has been carried out . Among diverse to be had snap shots, the goal images had been chosen on 1987 and 2000 year.

The selected photographs are visualized. The preference of statistics is defined by way of territorial and temporal suitability: both photos cover the same territory in Izmir environment, and temporally they have a reasonable time span (thirteen-years) which enables to carry out change detection in land cowl kinds. Also both snap shots are taken throughout summer time months: June 1987 and June 2000, which permits to investigate plant life kinds. The important chosen software is Erdas Imagine, since it provides extensive range of gear for raster photograph processing and spatial evaluation. The logical parts of studies paintings include hassle definition, reading existing research literature on the real subject matter, obtaining applicable facts, import and in step with-processing the statistics, spatial analysis of land cowl kinds, landscape mapping illustrating obtained results, first-rate assessment and final end. The vital technical part of paintings has photo type, which is the key method for the present day studies.

The Landsat TM photograph that has 7 wavebands and incorporates information of 512 rows and columns. Therefore, each Landsat scenes had been captured a series of GeoTIFF files with separate TIFF document for each of bands, respectively At the following operating step the Landsat bands had been unpacked for every image. Then those TIFF layer bands of Landsat have been imported from the preliminary layout (.Tiff) to Erdas Imagine layout (.Img). To facilitate work with imagery, the default directory each for enter and output documents has been set up in 'Preferences' to contemporary working folder. The scheme summarizing the workflow of current researcH. During the following operating step, the seven Landsat bands were imported into a unmarried Erdas Imagine document (.Img) for each photo. The special Landsat TM bands constitute a multispectral statistics set, so the color composite, or fake coloration composite, is constituted of pictures acquired in extraordinary bands. Different mixtures had been tried to obtain top-quality one (Dobesova, 2021). Thus, a popular actual color composite of picture information is represented by means of corresponding colorations: crimson, inexperienced and blue spectral regions are assigned red, inexperienced and blue photo bands, respectively. But for the fake colour composite they're displayed within the inverse way, i.E. By way of setting the infrared, crimson, inexperienced within the pink, inexperienced and blue channels, respectively.

Indices are used to create output snap shots through mathematically combining the DN values of numerous bands. These ratio photographs are derived from the absorption/mirrored picture spectra of fabric of hobby. The absorption is primarily based on the molecular bonds within the (floor) fabric. Thus, the ratio regularly offers records on the chemical composition of goal In many instances, judiciously selected indices can spotlight and decorate variations that can not be placed in the show of original shade bands (Landsat TM IR band: 4 R band: three; Landsat MSS IR band: 7 R band: five) LANDSAT Landsat has been supplying a nearly continuous file of world land ground change thinking about 1972. This file represents one of most ordinary to be had records of latest earth facts statistics, and its use has facilitated knowledge of earth ground methods throughout spatial and temporal scales and disciplines.

Features of ERDAS IMAGINE

Due to similarity of each orbital tendencies of Landsats 4, five and seven (Landsat 6 failed upon release) and machine characteristics of TM and ETM+ sensors, records from the 2 sensors are in large element immediately similar in phrases of comparable spectral, spatial, temporal and radiometric choice. Therefore a tremendously uniform time series of information relationship once more from the discharge of Landsat four (sixteen July 1982) to prevailing time exists, offering a really beneficial dataset particularly for the long-term monitoring of terrestrial developments. The continuing manufacturing of TM data from Landsats 5 and 7 ensures the ability to immediately relate the contemporary dataset to Landsat information as a minimum into foreseeable future. This allows to with out trouble distinguish flora regions from the built-up areas.

Thus, clean and wholesome vegetation of rural fields is being represented in reddish shades, as flowers absorbs inexperienced coloration however displays infrared one. On the contrary, constructed-up areas of human settlements are normally appear as metallic grey, whilst you recall that those areas reflect identical quantities of close to infrared, purple and inexperienced. Color composition has been performed the usage of erdas imagine toolbar, raster/spectral/layer stack module (Becker ET AL., 2018). During this approach the man or woman layers had been merged into unmarried multi-layer “.Img” document. Since thermal band changed into essential (that is nr.6 for landsat tm), the merged layers covered only non-thermal bands of landsat (bands 1, 2, 3, 4, 5, and seven)

Band 1 (zero.45 - 0.52u m): provides accelerated penetration of water bodies and also able to differentiating soil and rock surfaces from flora and for detecting cultural capabilities.

Band 2 (0.52 - 0.60u m): it is touchy to water turbidity variations; it highlighted the turbid water inside the Barkley Lake. Because it covers the inexperienced reflectance top from leaf surfaces, it has separated plant life (forest, croplands with status vegetation) from soil. In this band barren lands urban areas and roads and highways have regarded as brighter (lighter) tone, however forest, plants, bare croplands, croplands with status crops have seemed as dark (black) tone. Also the Kentucky Lake has appeared as black tone (Shevchenko et al., n.d.).

Band 3 (zero.Sixty three - zero.69u m): senses in a strong chlorophyll absorption place and strong reflectance location for maximum soils. It has discriminated vegetation and soil. But it couldn’t separated water and wooded area. Forest land and water each have seemed as darkish tone. This band has highlighted barren lands, urban areas, avenue pattern within the urban area and highways. It has also separated croplands with status crops from naked croplands with stubble (Thakur and Jeet, n.d.).

Band four (zero.76 - 0.90u m): operates in the high-quality spectral vicinity to distinguish flora sorts and situations. Because water is a sturdy absorber of near IR, this band has delineated water bodies (lakes and sinkholes), outstanding among dry and wet soils (barren land and croplands). In this band croplands and grasslands have showed higher reflectance (brighter tone) than the forest (Nelson and Khorram, 2019). This band has additionally separated croplands from bare croplands. Since standing crops (plants) has higher reflectance within the near IR place, they have got seemed as brighter tone and because of presence of moisture content in the naked croplands, they have got appeared as darker tone. In the band four barren lands, urban areas and highways have no longer been highlighted and they regarded as darkish tone. Band four is beneficial for crop identity and emphasizes soilcrop and land-water contrast.

References

Atoma, H., Suryabhagavan, K.V. and Balakrishnan, M., 2020. Soil erosion assessment using RUSLE model and GIS in Huluka watershed, Central Ethiopia. Sustainable Water Resources Management, 6(1), pp.1-17

Becker, M., Schreiner, S., Auer, S., Cerra, D., Gege, P., Bachmann, M., Roitzsch, A., Mitschke, U. and Middelmann, W., 2018, October. Reconnaissance of coastal areas using simulated EnMAP data in an ERDAS IMAGINE environment. In Earth Resources and Environmental Remote Sensing/GIS Applications IX (Vol. 10790, p. 107900L). International Society for Optics and Photonics

Chavan, A., Sarkar, S., Thakkar, A. and Bhandari, S., 2021. A New Method of Mosaicking Context Camera (CTX) Images for the Geomorphological Study of Martian Landscape. Open Journal of Geology, 11(8), pp.373-380

Dobesova, Z., 2021. Cognition of Graphical Notation for Processing Data in ERDAS IMAGINE. ISPRS International Journal of Geo-Information, 10(7), p.486

Nelson, S.A. and Khorram, S., 2019. Image Processing and Data Analysis with ERDAS IMAGINE®. CRC Press

Ruiz, D.A., Bacca, E.B. and Caicedo, E.F., 2019. A Tool for Analysis of Spectral Indices for Remote Sensing of Vegetation and Crops Using Hyperspectral Images. Entre Ciencia e Ingeniería, 13(26), pp.51-58

Shevchenko AV, Dvigalo V, Zorn E, Stefanova Vassileva M, Walter T, Svirid I, Chirkov S. Shiveluch volcano 2012-2019 photogrammetric dataset

Thakur, S. and Jeet, I., Detection of Land Surface Temperature Using Computer aided Geographic Information System Tool

Twumasi, Y.A., Merem, E.C., Namwamba, J.B., Okwemba, R., Ayala-Silva, T., Abdollahi, K., Lukongo, O.E.B., Tate, J., La Cour-Conant, K. and Akinrinwoye, C.O., 2020. Use of GIS and Remote Sensing Technology as a Decision Support Tool in Flood Disaster Management: The Case of Southeast Louisiana, USA. Journal of Geographic Information System, 12(02), p.141

Xi, W., Zhou, X. and Zhang, J., 2020. LANDISVIEW 2.0: An upgraded visualization and analytical tool for landscape modeling. Environmental Modelling & Software, 134, p.104849