scholarly journals SEPARATING THE TERRAIN COVER OF IRAQI MARSHES REGION USINQ NEW SATELLITE BAND COMBINATION

2020 ◽  
Vol 51 (6) ◽  
pp. 1504-1516
Author(s):  
Abduljabbar & Naji
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Spectral Response ◽  
Region Of Interest ◽  
New Method ◽  
Support Vector ◽  
Green Band ◽  
Band Combination ◽  
Landsat 5 Tm ◽  
Band Combinations

A band combination (542) has been adopted and applied as a new method to classify the Iraqi marshes regions which they are located in the southern of Iraq using Landsat-5 TM scene. The results of proposed band combination were compared to the standard band combinations which they are selected to classify scene classes (541, 543 and 742). In addition, the results reveal that the standard band combinations are failed to discriminate between the scene classes that due to the aquatic nature of the scene, which makes the spectral response of the different classes very close, thus, they miss-classify the scene. Furthermore, the green band which was used in the proposed band combination enhanced the spectral response to discrimination between the different land cover classes. It was found that the support vector machine technique that performed to classify the scenes was revealed to be a very good classifier. The contribution of this study is obvious as the resulting outcomes can be capitalized as guidelines to separate the land cover classes in the aquatic nature to an accuracy that has been reached to 98% compared with the scene’s region of interest.

scholarly journals A New Approach for Remote Sensing Image Sample Selection Based on Convex Theory

2015 ◽  
Vol 11 (4) ◽  
pp. 14 ◽  
Author(s):  
Pan Xin ◽  
Hongbin Sun
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Land Cover ◽  
Sample Selection ◽  
Region Of Interest ◽  
Computation Time ◽  
Support Vector ◽  
Machine Model ◽  
Remote Sensing Imagery ◽  
Remote Sensing Classification

Advancements in remote sensing technology have led to improvements in the acquisition of land cover information. The extraction of accurate and timely knowledge about land cover from remote sensing imagery largely depends on the classification techniques used. Support vector machine has been receiving considerable attention as a promising method for classifying remote sensing imagery. However, the support vector machine learning process typically requires a large memory and significant computation time for treating a large sample set, in which some of the samples might be redundant and useless for the support vector machine model training. Therefore, higher-quality and fewer samples from the sample selection should be utilized for support vector machine-based remote sensing classification. A convex theory-based remote sensing sample selection algorithm for support vector machine classifiers is developed in this work. A Landsat-5 Thematic Mapper imagery acquired on August 31, 2009 (orbit number 113/27) is adopted in our experiments. The study area's land cover/use was divided into five categories. Using the region of interest tool, we select samples from the image of the study area, with each category consisting of 1000 independent pixels. Results show that for most cases, our method can achieve higher classification accuracy than random sample selection method.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

2021 ◽  
Vol 64 (3) ◽  
pp. 265-274
Author(s):  
Fatima Mushtaq ◽  
Khalid Mahmood ◽  
Mohammad Chaudhry Hamid ◽  
Rahat Tufail
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Support Vector ◽  
Kappa Statistics ◽  
Error Matrix ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
Hyper Plane ◽  
Better Than

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.  

scholarly journals Tinjauan Algoritma RoI (Region of Interest) Dengan Metode Pengambangan Otsu Dan Klasterisasi K-Mean; Hasil Dan Tantangannya

2020 ◽  
Vol 16 (2) ◽  
pp. 75
Author(s):  
Didit Widiyanto
Keyword(s):  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Region Of Interest ◽  
Naïve Bayes ◽  
Support Vector ◽  
Gray Level

Akurasi sebuah klasifikasi citra ditentukan oleh pengklasifikasi.  Meskipun RoI (Region of Interest) tidak menentukan secara langsung akurasi, namun RoI menentukan lingkup klasifikasi citra.   Terdapat tiga algoritma yang dapat digunakan sebagai algoritma RoI yaitu; Balanced Histogram Thresholding (BHT), algoritma Otsu, dan algoritma klasterisasi K-Means.  Paper ini meninjau algoritma Otsu dan algoritma klasterisasi K-Means yang digunakan oleh lima peneliti.  Dari ke lima peneliti; tiga peneliti menerapkan algoritma Otsu dan dua peneliti menerapkan algoritma K-Means sebagai algoritma RoI. Setelah operasi RoI, ke lima peneliti menerapkan algoritma GLCM (Gray Level Co-occurance Matrix) sebagai pengekstraksi ciri tekstur.  Hasil ekstraksi ciri diklasifikasi dengan menggunakan berbagai pengklasifikasi antara lain SVM (Support Vector Machine), Naive Bayes, dan Decision Tree. Akhirnya dengan membandingkan hasil dari ke lima peneliti, akurasi tertinggi diperoleh sebesar 100% dengan pengklasifikasi SVM menggunakan algoritma Otsu sebagai algoritma RoI, dan akurasi terendah adalah sebesar52% yang menggunakan algoritma Otsu pada kanal S dari citra HSV (Hue, Saturation Value).

scholarly journals Preliminary verification for application of a support vector machine-based cloud detection method to GOSAT-2 CAI-2

2018 ◽  
Vol 11 (5) ◽  
pp. 2863-2878 ◽  
Author(s):  
Yu Oishi ◽  
Haruma Ishida ◽  
Takashi Y. Nakajima ◽  
Ryosuke Nakamura ◽  
Tsuneo Matsunaga
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Greenhouse Gases ◽  
Visual Inspection ◽  
Fiscal Year ◽  
Tropical Rainforests ◽  
Support Vector ◽  
Accuracy Evaluation ◽  
Data Set ◽  
Land Cover Types

Abstract. The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009 to measure global atmospheric CO2 and CH4 concentrations. GOSAT is equipped with two sensors: the Thermal And Near infrared Sensor for carbon Observations (TANSO)-Fourier transform spectrometer (FTS) and TANSO-Cloud and Aerosol Imager (CAI). The presence of clouds in the instantaneous field of view of the FTS leads to incorrect estimates of the concentrations. Thus, the FTS data suspected to have cloud contamination must be identified by a CAI cloud discrimination algorithm and rejected. Conversely, overestimating clouds reduces the amount of FTS data that can be used to estimate greenhouse gas concentrations. This is a serious problem in tropical rainforest regions, such as the Amazon, where the amount of useable FTS data is small because of cloud cover. Preparations are continuing for the launch of the GOSAT-2 in fiscal year 2018. To improve the accuracy of the estimates of greenhouse gases concentrations, we need to refine the existing CAI cloud discrimination algorithm: Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1). A new cloud discrimination algorithm using a support vector machine (CLAUDIA3) was developed and presented in another paper. Although the use of visual inspection of clouds as a standard for judging is not practical for screening a full satellite data set, it has the advantage of allowing for locally optimized thresholds, while CLAUDIA1 and -3 use common global thresholds. Thus, the accuracy of visual inspection is better than that of these algorithms in most regions, with the exception of snow- and ice-covered surfaces, where there is not enough spectral contrast to identify cloud. In other words, visual inspection results can be used as truth data for accuracy evaluation of CLAUDIA1 and -3. For this reason visual inspection can be used for the truth metric for the cloud discrimination verification exercise. In this study, we compared CLAUDIA1–CAI and CLAUDIA3–CAI for various land cover types, and evaluated the accuracy of CLAUDIA3–CAI by comparing both CLAUDIA1–CAI and CLAUDIA3–CAI with visual inspection (400  ×  400 pixels) of the same CAI images in tropical rainforests. Comparative results between CLAUDIA1–CAI and CLAUDIA3–CAI for various land cover types indicated that CLAUDIA3–CAI had a tendency to identify bright surface and optically thin clouds. However, CLAUDIA3–CAI had a tendency to misjudge the edges of clouds compared with CLAUDIA1–CAI. The accuracy of CLAUDIA3–CAI was approximately 89.5 % in tropical rainforests, which is greater than that of CLAUDIA1–CAI (85.9 %) for the test cases presented here.

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