Top PDF Change Detection in Landuse/ Landcover of Abeokuta Metropolitan Area, Nigeria Using Multi-Temporal Landsat Remote Sensing

Change Detection in Landuse/ Landcover of Abeokuta Metropolitan Area, Nigeria Using Multi-Temporal Landsat Remote Sensing

Change Detection in Landuse/ Landcover of Abeokuta Metropolitan Area, Nigeria Using Multi-Temporal Landsat Remote Sensing

CHANGE DETECTION IN LANDUSE/ LANDCOVER detection of land use/land cover before the invention of Remote Sensing and Geographic Information System (GIS) (Asiyanbola et al., 2014). Remote Sensing and Geographic Information System (GIS) are advanced methods in capturing, digitizing, analysing, processing and interpreting land use/land cover data. These two methods have enhanced the accuracy and authencity of land use/cover change analysis over period of time. Abeokuta being the capital of Ogun State and one of the urban centers in Nigeria had gone through tremendous developmental changes which include road expansion, deforestation, building of infrastructures and other human activities since its creation in 1976. The urban renewal for instance of the present Administration had revolutionised the landscape of the study area. These activities have resulted into the opening of some areas for major developments, increased land conversion which has altered and modified the landscape over time. So, there is need for comprehensive capturing and analysis of changes in the land use/land cover. Attempts were made in previous studies to model the change detection in land use/land cover of Abeokuta in the past (Ufoegbune et al., 2008), but due to recent extensive and massive land conversion in the study area, there is an urgent need for updating and having a comprehensive land use land cover data of the area using advanced GIS and Remote Sensing techniques. Therefore, the aim of this study is to examine the changes that have occurred to the land use/land cover of the study area over a period of 31 years, precisely 1984 -2015.
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Landuse and Landcover Change Detection of Jebba Lake Basin Nigeria: Remote Sensing and GIS Approach

Landuse and Landcover Change Detection of Jebba Lake Basin Nigeria: Remote Sensing and GIS Approach

Many researchers have applied Remote Sensing/Geographic Information System (GIS) to study the landuse and landcover change detection around artificial lakes all over the world. Mattikalli (1995) applied Remote Sensing and GIS to the landuse of the River Glen catchments in England by acquiring data from 1931 to 1989. His work revealed that much of the grassland changed to arable land during the study area. Okhimanhe (1993) also used the combination of Spot HRV imagery of 1986 and aerial photographs of 1974 to study the environmental impact assessment of Bunimburum/Tiga dam in Kano state. Nigeria. The work revealed that the construction of Tiga dam contributed to the depletion of the vegetation that could have helped stopped desert encroachment. Adeniyi and Omojola (1999). used aerial photographs, Landsat MSS, Spot XS/Panchromatic Image Transparency and Topographical maps to study landuse/landcover changes in Sokoto and Guronyo dams, Nigeria, between 1962 and 1986. Their work revealed that settlement covered most part of the area before and after the construction of the dam. Ikusemoran (2003) used Landsat multispectral landuse and vegetation cover maps of 1978 and 1995 in combination with 1965 aerial photographs to study the landuse and landcover changes of Kainji lake basin. The study revealed that the lake reservoir was expanding with increasing agricultural activities around the lake.
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Multi-Temporal Change Detection at a Limestone Mining and Cement Production Facility in Central Nigeria

Multi-Temporal Change Detection at a Limestone Mining and Cement Production Facility in Central Nigeria

Abstract: Limestone mining and cement production at Yandev, Nigeria commenced in 1980 without an environmental impact assessment (EIA) to ascertain the extent of impact these activities could bring to bear on the physical and living conditions of the host environment. This study was carried out to assess the impact that mining of limestone and production of cement has inflicted on the quality and density of vegetation within the study area about 32 years since production commenced. Multi-temporal satellite imageries of the study area (Landsat for 1976, 1986, 1996 and Nigeriasat-1 for 2006), ILWIS Academia 3.3 and SPSS Version 15 were used for data analyses. Landuse and landcover (LULC) change detection; land surface temperature (LST) extraction; and normalized differentials of vegetation index (NDVI) estimation were carried out. The paired t-test was used for landcover data analysis. The study discovered first, that LULC changes occurred with built-up area increasing from 0.05 km 2 in 1976 to 1.51 km 2 by 2006, thus representing the landcover category with the highest gain. Conversely, thick vegetation declined from 4.30 km 2 in 1976 to 1.51 km 2 in 2006. Thick vegetation category lost to all other landcover categories while gaining only 0.07 from water bodies. The projected LULC of the study area by 2015 reveal an expected expansion in built-up area from 1.51 km 2 in 2006 to 1.90 km 2 by 2015, whereas thick vegetation is expected to further decline from 1.51 km 2 in 2006 to 0.80 km 2 by 2015. Second, the LST have risen over the study epochs (1976, 1986 and 1996) while NDVI signifies decline in quality and health status of vegetation cover over the study period (1986, 1996 and 2006). The study concludes that there is rapid decline in density and quality of vegetation cover within the study area. Ameliorative measures are recommended to include reforestation and improvement in limestone mining methods/techniques amongst others.
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An Evaluation of Urban Landcover Dynamics using Remote Sensing: A Case Study of Ibadan, Oyo State, Nigeria

An Evaluation of Urban Landcover Dynamics using Remote Sensing: A Case Study of Ibadan, Oyo State, Nigeria

Satellite remote sensing has been demonstrated as a useful tool to capture data that are relevant for the analysis of urban landuse patterns .A remote sensing device records response which is based on many characteristics of the land surface, including natural and artificial cover. It is accurate for mapping due to ease of interpretation, it uses the element of tone, texture, pattern, shape, size, shadow, site and association to derive information about land cover. It is often believed that no single classification could be used with all types of imagery and all scales [11]. Remote sensing provides tremendous means and ways of classifying landuse land cover change due to the synoptic and repetitive coverage capability that can be used to identify and monitor changes at regional and global scales [12]. Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times [13]. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution. Early Markovian analysis is used as a descriptive tool to predict land use change on a local or regional scale [14]. Three different Markov process models were developed for prediction, firstly solely from the changed area, without giving consideration to the spatial information around it. Secondly, the spatial neighbourhood information were considered, but with different strategies, while the other one applies the four nearest neighbour consideration only to the pixels under boundary condition. [15; 7]. Therefore, this study uses Remote Sensing and GIS techniques for landcover evaluation of Ibadan and environs for proper facilitation of landcover and map landcover of Ibadan using Satellite images of 1972, 1984, 2000 and 2006; and determine the trend and rate of landcover changes within the period 1972 to 2006 and also to predict changes in landcover in the future.
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Geospatial Analysis of Landuse and Landcover Dynamics In Ado Ekiti, Nigeria

Geospatial Analysis of Landuse and Landcover Dynamics In Ado Ekiti, Nigeria

Remote Sensing and GIS techniques was used to analyse the landuse and landcover dynamics of Ado-Ekiti LGA, the Ekiti State capital, for a period of twenty nine years (29yrs) in this study. Multi-temporal and multi-source satellite imageries of Landsat 1986, 1991, 2002 and 2015 were used. The study employed supervised digital image classification method using ENVI 5.3 software and five landuse and landcover types which include settlement, bare surface, cultivation, forest and water body were detected and captured as polygon. The area in square kilometers of each land use type in each year was calculated and thereafter the change was determined by subtracting the area of the same land use type in 1986 from 2015, the percentage and magnitude of change are therefore calculated. The results obtained shows that the settlement has increased by 47km 2 (79%) while cultivation, forest and water body reduced by 45.53km 2 (9.98%), 61.7km 2 (13.79%) and 0.03Km 2 (6.5%) between 1986 and 2015. The statistics also reveals that substantial land use/land cover changes have taken place and that the settlement and baresurface areas have continued to expand over the study period while the forest and farmland have decreased. The study also notes that the expansion of the urban settlement areas has resulted into reduction of the land under agriculture and other natural vegetation, thereby affecting the natural ecosystems habitat quality, which has consequently led to environmental degradation.
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Spatio Temporal Analysis of Landuse/Landcover Change of District Pishin Using Satellite Imagery and GIS

Spatio Temporal Analysis of Landuse/Landcover Change of District Pishin Using Satellite Imagery and GIS

Detecting change on the face of the globe using GIS (Geographic Information System) aided by re- motely sensed imagery is now becoming an indispensable tool in managing the resources of our planet. The present study with the help of GIS and remote sensing (RS) is also a similar attempt in recording and quantifying change in land use and land cover in district Pishin both in spatial and temporal extents. Satellite imagery was acquired from the USGS official website from three LANDSAT satellites. Theses satellites are LANDSAT 5, LANDSAT7 and LANDSAT 8. The data were acquired for the years 1992, 2003 and 2013. Satellite imagery was processed in ArcMap 10.1 and maximum likelihood supervised image classification was applied in reaching the goal of detecting change. The result of the analysis revealed that built-up area was increased by 5.84%; vegetation was increased by 3.89%; water bodies were increased by 0.05% and bare surfaces were decreased by 9.78%. The decrease in the barren surfaces was attributed to the increase in vegetation and built-up area which replaced the barren land in the study area. This paper also shows the signifi- cance and potential of digital change detection methods in managing the resources of our envi- ronment and keeping an eye on the land use and land cover of our Earth.
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Landuse and Landcover analysis using Remote Sensing and GIS: A Case Study in Uravakonda, Anantapur District, Andhra Pradesh, India

Landuse and Landcover analysis using Remote Sensing and GIS: A Case Study in Uravakonda, Anantapur District, Andhra Pradesh, India

The change detection of the study advocates that multi temporal satellite imagery plays a important function in quantifying spatial and temporal phenomena which is in any other case now not possible to try through conventional mapping. study reveals that the major land use in the study area is barren/waste land. The area under barren/waste land has increased by 8.00% (31.5 sq.km) due to deforestation work during 2000 to 2010. The second major category of land in the study area is agriculture which was decreased by 6.4% (25.5 sq.km) due to adaptation in vegetation, barren land and built-up land. The third major category of land in the study area is water bodies have also decreasing. During the study period (i.e., 2000–2010), built- up land has been increased by 3.5% (13.8 sq.km) due to alteration into urbanization and industrial areas. Thus, the present study illustrates that remote sensing and GIS are important technologies for analysis and quantification of spatial phenomenon which is otherwise not possible to attempt through conventional mapping techniques. Change detection is made possible by these technologies in less time, at low cost and with higher accuracy.
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Assessment of Changes in Landcover and Landuse in Ebonyi State, Using Remote Sensing Technique

Assessment of Changes in Landcover and Landuse in Ebonyi State, Using Remote Sensing Technique

This study shows a 31 years multi-temporal changes in landuse and landcover in Ebonyi State, Southeastern Nigeria. Since 1996 when the study area became a State, Ebonyi has experienced significant changes in its landuse and landcover as a result of its fast urbanization process. Based on the unique advantage that multi-temporal observation offers, landsat imageries of 1984, 2000, 2007 and 2015 were sourced and analyzed using Erdas Imagine 9.2 and ArcGIS 10.2 tools for the spatio-temporal changes in landcover extent and also rate of change from 1984 to 2015. This study was carried out essentially to draw attention of all stakeholders in the urban development sector for the need to restrict those negative adverse changes arising from urbanization that could be detrimental to the environment. Analysis of imageries revealed that the extent of built –up areas in Ebonyi State increased from 42074.39 hectares in 1984 to 212762.3 hectares in 2015 representing a percentage increase of 35.8487%. While swamp rice farms gained (15.6850%) however, vegetation cover lost as much as 48.4662% in the 31 years period the study covered. The decrease in vegetation cover was found to have been replaced by increases in built –up and swamp rice farms areas. The study however, concluded that adequate measure must be taken to curtail the rate at which vegetation is been exploited for human use as it could led to harsh adverse effect that could be detrimental to the environment. In addition, more people should be encouraged to plant trees so as to ameliorate the negative impact of these changes on the environment as well as humans. Institutions in charge of urban planning should also be strengthened for effective monitoring of urban expansion.
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An analysis of landuse and landcover in Tiruchirappalli district using least square method

An analysis of landuse and landcover in Tiruchirappalli district using least square method

squares statistical method.LSM (Least Square Method) is one of the techniques of Statistics. Anderson (1971) studied the land use classification using GIS. Byrant (1976) studied the interaction among the forces and the effects of land use changes on associated attributes such as land quality and land value. These investigations offer further insight into lands, enhance the knowledge, and provide a sound basic from which both management and planning policies, regarding land resources can be made. Rhind and Hudson (1980) described the land use studies with reference to agriculture and urban la use pattern. Jensen (1981) explains change detection that can be performed manually by means of visual digital change detection techniques. Gautam et al. (1982) dealt with the technique of remote sensing and how far it helps in the rapid
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Land Cover Mapping of the Greater Cape Three Points Area Using Landsat Remote Sensing Data

Land Cover Mapping of the Greater Cape Three Points Area Using Landsat Remote Sensing Data

Hierarchical terms which will be used in this study, they will be briefly described here. The structural terms of open and closed describe the amount of landcover being occupied by that land cover class within the minimum mapping unit. Closed describes a class which occupies greater than 60 – 70 percent of the total area of a minimal mapping unit (MMU), open describes a class which occupies less than 70 percent but more than 20 percent MMU and sparse describes a class which occupies less than 20 percent but more than 1 percent of the MMU. Next, the term mosaic describes a class in which two or three individual land cover types share space within one MMU. There are two possible scenarios in which this type of land cover class becomes possible. The first is when each class is a spatially separate entity, such as agricultural fields within a forest. The second is when these classes are in an intricate mixture such as rainfed cultivated field with interlaced woodlands. When dealing with this scenario, the sequence of class names within the mixed mapping unit represents the
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Land Use/Land Cover Change Detection by Multi-Temporal Remote Sensing Imageries: Bangalore City India (1992-2012)

Land Use/Land Cover Change Detection by Multi-Temporal Remote Sensing Imageries: Bangalore City India (1992-2012)

two other South Indian states, Tamil Nadu and Andhra Pradesh.. Since the 1980s, Bangalore has enjoyed the reputation of being one of the fastest growing cities in Asia The Bangalore metropolitan area covers an area of 725 sq km, and is the fifth largest city in India. The mean annual total rainfall is about 880 mm with about 60 rainy days a year over the last ten years. The summer temperature ranges from 18 °C to 38 °C, while the winter temperature ranges from 12 °C to 25 °C. Thus, Bangalore enjoys a salubrious climate all year round
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A Comparison of Change Detection Analyses Using Different Band Algebras for Baraila Wetland with Nasa’s Multi Temporal  Landsat Dataset

A Comparison of Change Detection Analyses Using Different Band Algebras for Baraila Wetland with Nasa’s Multi Temporal Landsat Dataset

data were done and the accuracy was checked, it was assumed that NDWI index is best suited for the present site. However, all the Radiometric results show a definite decline in surface water of the wetland in this period, most noticeably in last five years the brisk rate of reduction in surface water has resulted in lowering it to nearly half of its size. The statistical result on the basis of most accurate index which is NDWI reveals that total wetland area in 1989 was 15.116 km 2 out of which most abundant wetland feature was surface water which was nearly 55% of the whole wetland area. This wetland feature is reduced heavily in last five year and now it covers only 24.53% of the total wetland. The area with Swamp has more than doubled from 21.40% to nearly 45% in last quarter decade while area with aquatic weeds has also increased from 3.584 km 2 in 1989 to 4.605 km 2 in 2014 (Graphs 1-5).
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CHANGE DETECTION OF REMOTE SENSING IMAGES USING LEVY FLIGHT PSO

CHANGE DETECTION OF REMOTE SENSING IMAGES USING LEVY FLIGHT PSO

Due to diverse applications such as medical diagnosis, industrial production, video surveillance and land cover changes monitoring, change detection[1-3] has gained great interest in the recent decades. In this paper the emphasis is on geographical change detection using remote sensing imageries as the rationale behind this is manifold. The first fact is the availability of satellite images of different resolution and various technologies present in processing and analyzing them. At the same time, the challenges arising in real-time problems such as disaster management, protecting ecology and monitoring land use changes are increasing and therefore, developing potential solutions to handle these complexities have become the need of the hour. Though several methods are available today, many of them are inefficient in detecting the actual changes occurred when the nature of changes involved are heterogeneous, and which makes this research relevant. In order to detect changes occurred, two images of the same scene acquired at two time stamps are required. Since the quality of remote sensing images are prone to be affected by many external factors while capturing, it has to be pre- processed for minimizing such effects. The radiometric errors due to atmospheric haze, sun angle, azimuth etc. are rectified by suitable software. Geometric errors are corrected by co-registering the images to the same co- ordinates. Filtering operations are often done for removing the noise inherent in the images while capturing. After preprocessing operations, the difference image(DI) is generated, for which, two commonly adopted methods are rationing and subtraction [3]. While the former employs ratio operation on corresponding pixels of the two input images, the latter generates the DI by subtracting the pixels of the input images. Finally, the labeling of pixels into two groups - changed and unchanged- are done to produce a binary change map. For this, the techniques often used are segmentation in the supervised or unsupervised paradigm[4] or by the usual thresholding method. From the literature, it
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Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images

Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images

Abstract: Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming. Furthermore, numerous methods are data-dependent. Therefore, their degree of automation should be improved significantly. Three techniques, which consist of a semi-automatic change detection system, are proposed for LCCD to overcome the abovementioned drawbacks. The three techniques are as follows: (1) change magnitude image (CMI) noise reduction is based on Gaussian filter (GF), which is coupled with OTSU for reducing CMI noise automatically using an iterative optimization strategy; (2) a method based on histogram curve fitting is suggested to predict the threshold range for parameter determination; and (3) a modified region growing algorithm is built for iteratively constructing the final change detection map. The detection accuracies of the proposed system are investigated through four experiments with different bi-temporal image scenes. Compared with several widely used change detection methods, the proposed system can be applied to detect land cover change with high accuracy and flexibility. This work is an attempt to provide a change detection system that is compatible with remote sensing images with high and median-low spatial resolution.
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Intelligent Division for Change Detection in Multi spectral Remote sensing Images

Intelligent Division for Change Detection in Multi spectral Remote sensing Images

CHANGE Detection(CD) is a stand out amongst the most essential application in remote detecting innovation .The point of CD is to discover pixels that compare to genuine changes on the ground in sets of co-registered pictures obtained over the same geological region at two distinct times .Typically, CD strategies depend on the of that, the Distinction Images (DI) from two co-registered pictures, and after that, progression are distinguished via naturally dividing the DI into two areas connected with changed and unaltered(no-change) classes, separately. None the less, as these strategies are information driven. The completely programmed separation in the middle of changed and unaltered classes is compelled by the unpredictability of the measurable dispersions portraying these classes, their level of cover, and introduction. as of late , the use of self-loader strategies with client’s mediation (i.e., intelligent division) has ended up famous in the writing of picture handling . They speak to a promising answer for upgrading and summing up
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Land Use and Land Cover Change Detection in Ilorin, Nigeria, Using Satellite Remote Sensing

Land Use and Land Cover Change Detection in Ilorin, Nigeria, Using Satellite Remote Sensing

A good number of the respondents and/or interviewees maintained that the changes in the classes over the 28 (1976 – 2004) years under review have had positive and negative effects on humans and the environment. For instance, 620 (31%) of the respondents were of the view that the city growth has brought about the availability of cheap labour in the study area. Several other positive effects are contained in fig 7. On the other hand, some of the identified basic negative aftermaths of the changes include pressure on land (92.46%); deforestation (281:14.0%) and lack of employment (130:6.5%). Others are reflected in Table 5.
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Change Detection On Klang  Area Using Landsat S-TM Data

Change Detection On Klang Area Using Landsat S-TM Data

Matlamat utama projek ini adalah untuk mengesan perubahan di permukaan tanah di kawasan Klang dengan menggunakan data daripada Landsat-5 TM. Projek ini melibatkan kaedah Change Detection dan remote sensing bagi mengesan perubahan dengan membandingkan dua imej satelit pada kawasan yang sama tetapi berlainan masa. Penyiasatan struktur permuakaan tanah dan perubahan objek adalah berdasarkan imej satelit dengan menggunakan resolusi sederhana yang mencatatkan panjang gelombang pantulan pada permukaan sebanyak 0,4-0,7 mikrometer. Hasil akhir projek ini adalah dengan menghasilkan imej permuakan tanah yang telah diproses. Imej tersebut dipertingkatkan dengan menggunakan kaedah tanpa pengawasan iaitu K-Mean. Imej pertama adalah berbanding dengan imej yang dipertingkatkan untuk melihat sama ada terdapat perubahan pada imej tersebut dari segi struktur tanah dan juga objek. Imej yang telah diproses boleh memberi maklumat kepada pengguna akhir tentang keadaan tanah atau perubahan objek dalam satu tempoh masa dengan melihat hasil akhir daripada imej.
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Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Mr.  TongFei  Liu  and  Dr.  YiXiang  Chen  did  the  experiments  and  provided  several  helpful  suggestions.  333. Prof.PengLin Zhang provided contributions to improve the quality of[r]

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Change detection analysis of Talcher coalfield

using Remote Sensing and GIS

Change detection analysis of Talcher coalfield using Remote Sensing and GIS

The change in land cover as a result of anthropogenic activities has played a major role in global environmental change and hence has become a hot spot for researchers (Liu et al., 2002). It is the process of identifying variations in an object or phenomenon by observing it at different times (Singh, 1989). The alteration in land cover including tropical deforestation has attracted worldwide attention because of its potential effects on soil erosion, run-off and carbon dioxide level (Joshi et al., 2006). Change detection compares the differences in spectral signatures from images taken of the same area in different time periods (Don, 2011). The detailed process involves superimposing maps of more than one time period over each other to find the change (Jessica et al., 2001).
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An Empirical Investigation on Thematic Accuracy
          of Landuse/Landcover Classification Using Fused
          Images

An Empirical Investigation on Thematic Accuracy of Landuse/Landcover Classification Using Fused Images

Abstract: The satellite images at different spectral and spatial resolutions with the aid of image processing techniques can improve the quality of information. Especially image fusion is very helpful to extract the spatial information from two images of different resolution images of same area. An operation of image analysis such as image classification on fused images provides better results in comparison of original data. The comparisons of various fusion techniques have been discussed and their accuracies have been evaluated on their respected classifications. By using the digital image classification techniques such as supervised and unsupervised classification methods, the study reveals that all the fused images have higher information than the original images. In this study to demonstrate the enhancement and accuracy assessment of fused image over the Multispectral images using ERDAS Imagine 9.1 Software.
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