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Register a new userImprovement the application of unsupervised classification algorithms on remote sensing images using PCA technique
تحسين تطبيق خوارزميات التصنيف غير المراقب على صور الاستشعار عن بعد باستخدام تقنية تحليل المركبات الأساسية
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قمنا في هذا البحث بدراسة خوارزمية من خوارزميات التصنيف غير المراقب تدعى خوارزمية التصنيف باستخدام عدد K من المتوسطات (K-means) فالخطوة الأساسية ليذه الخوارزمية ىي تحديد عدد K من العناقيد ثم حساب المسافة بين مركز كل عنقود و عناصر الصورة ليتم تخصيص هذه العناصر للعناقيد الأقرب بناء على قيمة مسافة مرجعية تدعى مسافة العتبة.
The basic step in this algori thm is determining the number of clusters (K) then calculating the distance between each cluster center and the elements of images to join each element to the closest cluster depending on threshold distance.
References used
Dovi V, and Kalaivani S, 2016-A view on spectral unmixing in hyperspectral images, Special Issue, Vol. 10, 23-32
Rodarmel C, and Shan J, 2002-Principle Component Analysis for Hyperspectral Image Classification, Surveying and Land Information System, Vol. 62, 115-123
Abburu S, and Babu S, 2015-Satellite Image Classification Methods and Techniques: A Review, International Journal of Computer Applications, Vol. 116, 20-25
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The study area (1485 km2) is located in the eastern part of the Syrian Desert
between the provinces of Deir al-Zour and Homs. Stretching from Kabbajeb to
Heer Palace. Due to the similarity of topography, vegetation and land cover, 48
samples were
collected one sample each 4 KM in order to predict the content of
the soil of calcium carbonate through satellite images Land Sat ETM7. The
spectral reflections of surface soil samples were recorded at laboratory using
spectroradiometer (FeildSpecPro®). The results of this study showed that the
presence of calcium carbonate at high rates in the soil increases the spectral
reflectivity values, and that the spectral domain TM 1B was the best to predict
the content of calcium carbonate in the soil.
The research aimed to study vegetation change detection of Lattakia province by
using remote sensing techniques, By applicating Normalized Differences Vegetation Index
(NDVI) due to what these techniques had from quickly, accuracy, and completely.
In
addition to saving efforts and money. that change detection methods have showed by
applicating it on Sentinel2 images the plant situation, it's area and distribution in the
studied area.
In addition to knowing plant's cover changes through time passing. Putting geo
databases which benefit in knowing plant situation, and the periodicity supervision for it's
changes.
The yearly and monthly changes of vegetation cover have been studied in Lattakia
province, By making change detection for plants cover of march between (2016-2017).
Then making change detection between march and august of 2017. It was observed that
there were no big changes, Whether increasing or decreasing for plants cover when
studying the yearly changes. While there was big decreasing of plants cover at western
plain areas of province when studying the monthly changes between march and august due
to raising of temperatures. And big increasing of vegetation in high areas.
Remote sensing is one of most important technology that provides information on
large areas in a short time. The study was carried out in Sweida governorate with the
aim of calculating the area of strategic crops and its distribution for the agricu
ltural
season 2014/2015 by classifying satellite images-type (BKA). The images were
received by the station at General Organization for Remote Sensing in Damascus,
Syria. The spatial resolution of the satellite BELARUSIAN SPACECRAFT is 10.2
meters. The results showed that the spread of the three crops (wheat, barley and
chickpea) was generally on the four sides of the governorate, especially barley
crop. Chickpeas and wheat concentrated in the west and center but in scattered
areas. The area of wheat crop according to image classification was 30494 ha which
accounted 8.97% of the studied area (Sweida governorate without Badia). The
degree of approach to the proportion of the Ministry of Agriculture was 95.19%.
The area of barley crop resulting from the classification process was 16705 ha,
which accounted 4.92% of the study area. While the area of barley according to the
statistics of Ministry of Agriculture was about 15933 ha. The area of chickpea crop
resulting from the classification process was 26063 ha which represented 7.67% of
the studied area. The results showed that the accuracy of the total classification was
82.4%, which allows satellite image to be used in calculating the area of strategic
crops and determine its locations and distribution.
Studying of land use changing Detection needs to the speed of implementation to
convoy the changes on the ground. the traditional ways in the analysis and visual
interpretation of the images and field studying need a lot of time and effort. So The
objective of this search is to classify group images (Landsat TM , ETM+) taken in different
dates automatically, and then to calculate the area of each land use/land cover, during the
years studied (1990 -2000- 2010) and comparison areas to identify the most important
changes occurring during that period.
In developing countries, where resources are often scarce, land availability, productivity potential, capability and sustainability for agriculture and, planning and maximizing the use of the land resources for a particular land utilization type is e
ssential. In order to ensure appropriate decision and, continued and sustainable productivity, thereby continuing to support the population economically without degradation, land use planning is essential. Remote sensing (RS) and geographic information systems(GIS) are useful tools in land use planning processes .in this study 4 landscape units (costal leveled plains ,valleys and channel bed, Piedmont :slight slopping ,moderately slopping ,sever slopping ,and Summit unit) were described and compared its characteristics with the land utilization requirements for 6 land utilization types (LUT1: irrigated citrus low mechanized ,LUT2: irrigated potato high mechanized ,LUT3: irrigated tomato medium mechanized,LUT4: rainfed olive low mechanized,LUT5:rainfed wheat low mechanized, LUT6:natural forests)by using LAMIS program .the results showed that43.07% of studied area is moderate suitability S3, 27.9% is low suitabilityS4 ,and 20.24% is unsuitable N1.the land suitability evaluation for LUT2 :35.18% good suitability S2 ,35.17% moderate suitabilityS3 and 20.24% low suitability S4 and for LUT3:50.97% good suitability S2,20.04% moderate suitabilityS3 and 20.24% low suitability S4. 43.07% of lands are very suitable S1 for LUT4 ,27.91% good suitability S2 and 20.24% moderate suitabilityS3.for LUT6 ; land suitability evaluation shown that 59.24% of lands are very suitable S1 and 31.93% good suitable S2.
Land use planning processes include matching between physical and socio-economic conditions by using Definite program .optimal land utilization types are determined depending on 3 multi-criteria ( costs ,gross margin , water requirements for irrigation) and according to two scenarios ( SC1:conservative scenario with concerned to water requirements , SC2:economic scenario with concerned to gross margin ).the results showed that LUT4 is the optimal current land use type of all three physiographic units (costal leveled plains ,valleys and channel bed and slight slopping).2 suggested land utilization types ( LUT7:irrigated kiwi low mechanized ,LUT8:irrigated groundnuts high mechanized) are proposed to costal leveled plains and compared with LUT4,comparison results shown that LUT4 is the optimal land use according to SC1 and LUT7 is the optimal land use type according to SC2.for valley and
channel bed unit , the optimal land use type according to SC1 is multiple land use type ( rainfed wheat under rainfed olive trees) and according to SC2 is LUT7.3 land utilization types(LUT7,LUT8,LUT9: rainfed lentil low mechanized)are suggested for slight slopping piedmont .the results of planning process showed that the optimal land use type is LUT4 for both scenarios SC1,SC2.
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