Analysis of Maximum Likelihood Classification on Multispectral Data

The aim of this paper is to carry out analysis of Maximum Likelihood (ML)classification on multispectral data by means of qualitative and quantitative approaches. ML is a supervised classification method which is based on the Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and can be estimated from the training pixels of a particular class. In this study, we used ML to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of ML

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy

PERBANDINGAN METODE KLASIFIKASI MAXIMUM LIKELIHOOD DAN MINIMUM DISTANCE PADA PEMETAAN TUTUPAN LAHAN DI KOTA LANGSA

ABSTRAKPembangunan daerah akan diikuti dengan kebutuhan akan lahan yang potensial.Kebutuhan lahan terus meningkat seiring dengan laju pertumbuhan penduduk.Kota Langsa merupakan Kota hasil pemekaran wilayah dari Kabupaten AcehTimur yang sedang melakukan perencanaan besar bagi kemajuan daerah tersebut.Penggunaan lahan pada Kota yang baru sangat berkaitan erat dari tutupan lahanyang ada di lahan tersebut, maka dari itu perlu dibuat peta tutupan lahan denganmetode yang cepat. Dengan tujuan penelitian ini yaitu untuk membuat petatutupan lahan dari Kota Langsa. Citra satelit yang digunakan dalam penelitian iniadalah citra satelit Landsat 8 tahun 2016. Teknologi untuk memetakan lahantersebut adalah teknologi penginderaan jauh. Terdapat berbagai metode klasifikasidalam memetakan tutupan lahan yaitu metode klasifikasi terbimbing dan tidakterbimbing. Pada kajian ini, menggunakan metode klasifikasi terbimbing.Evaluasi kesesuaian tutupan lahan di daerah kajian menggunakan dua metodeklasifikasi terbimbing yaitu Maximum Likelihood Classification (MLC) danMinimum Distance Classification (MDC). Kajian ini melakukan perhitungan ujiakurasi klasifikasi menggunakan tabel error matrix dan confusion matrix. Padahasil kajian terdapat perbedaan pada hasil akurasi dan hasil luasan tiap tutupanlahan yang dihasilkan. Pada Maximum Likelihood Classification (MLC) nilaiakurasi keseluruhan yaitu 86,0 % sedangkan pada Minimum DistanceClassification (MDC) hanya 75,0 %. Perbedaan luasan antara kedua metode yangdikaji juga berbeda, hasil luasan dari metode Maximum Likelihood Classification(MLC) lebih mendekati keadaan sebenarnya dari lokasi kajian dibandingkandengan metode Minimum Distance Classification (MDC). Kesimpulan dari kajianini yaitu Maximum Likelihood Classification (MLC) lebih baik digunakan dalammemetakan tutupan lahan di Kota Langsa dibandingkan dengan metode MinimumDistance Classification (MDC).Kata kunci : tutupan lahan, penginderaan jauh, Maximum LikelihoodClassification (MLC), Minimum Distance Classification (MDC)

IDENTIFIKASI LAHAN TAMBANG BATUBARA EKSISTING MENGGUNAKAN TEKNOLOGI PENGINDERAAN JAUH

ABSTRAKBatubara merupakan jenis bahan tambang yang digunakan untuk menunjangkehidupan manusia, batubara secara langsung dapat dimanfaatkan untukpembangkit listrik tenaga uap, industri semen, ketel uap dan briket dalam rumahtangga. Teknologi penginderaan jauh merupakan metode yang dapat digunakanuntuk pemantauan aktivitas penambangan, teknologi penginderaan jauhdigunakan karena dapat mengetahui suatu kondisi tanpa harus terjun langsung kewilayah kajian sehingga dapat menghemat biaya penelitian, dapat menjangkausemua tempat dan keakuratan relatif tinggi. Objek pada data penginderaan jauhdapat diamati dengan karakteristik titik, garis dan bentuk daerah yang berbedabeda.Lahan tambang batubara pada citra landsat 8 RGB 753 dicirikan denganadanya lahan yang berwarna jingga sampai dengan merah terang, bentuk lahanyang tidak beraturan, bertekstur kasar dan terdapat kubangan air yang tidakberaturan. Penelitian ini bertujuan untuk mengetahui sejauh mana teknologipenginderaan jauh dapat digunakan untuk mengidentifikasi lahan tambangbatubara eksisting di Kabupaten Aceh Barat melalui perhitungan nilai akurasiyang dihasilkan dari metode klasifikasi terbimbing Maximum LikelihoodClassification (MLC) dan Spectral Angle Mapper (SAM). Luasan lahan tambangbatubara yang dihasilkan metode Maximum Likelihood Classification (MLC)adalah 74,71 Ha dengan total akurasi 89,00%, nilai akurasi kappa 38,20%.Luasan lahan tambang yang dihasilkan metode Spectral Angle Mapper (SAM)adalah 62,73 Ha dengan total akurasi 83,00%, nilai akurasi kappa 13,44%. Totalakurasi hasil klasifikasi menggunakan teknologi penginderaan lebih besar dari75,00% menunjukkan metode Maximum Likelihood Classification (MLC) danSpectral Angle Mapper (SAM) mampu mengidentifikasi lahan tambang batubaradan non tambang batubara. Berdasarkan nilai akurasi yang dihasilkan dari keduametode, metode Maximum Likelihood Classification (MLC) lebih bagus dari padametode Spectral Angle Mapper (SAM) dalam mengidentifikasi lahan tambangbatubara eksisting.Kata kunci : Batubara, Penginderaan Jauh, Maximum Likelihood Classification(MLC), Spectral Angle Mapper (SAM)

Atmospheric correction analysis on LANDSAT data over the Amazon region

The Amazon Region natural resources were studied in two ways and compared. A LANDSAT scene and its attributes were selected, and a maximum likelihood classification was made. The scene was atmospherically corrected, taking into account Amazonic peculiarities revealed by (ground truth) of the same area, and the subsequent classification. Comparison shows that the classification improves with the atmospherically corrected images

The Effects of Haze on the Accuracy of Maximum Likelihood Classification

This study aims to investigate the effects of haze on the accuracy of Maximum Likelihood classification. Data containing eleven land covers recorded from Landsat 5 TM satellite were used. Two ways of selecting training pixels were considered which are choosing from the haze-affected and haze-free data. The accuracy of Maximum Likelihood classification was computed based on confusion matrices where the accuracy of the individual classes and the overall accuracy were determined. The result of the study shows that classification accuracies declines with faster rate as visibility gets poorer when using training pixels from clear compared to hazy data

Comparison of the MPP with other supercomputers for LANDSAT data processing

The massively parallel processor is compared to the CRAY X-MP and the CYBER-205 for LANDSAT data processing. The maximum likelihood classification algorithm is the basis for comparison since this algorithm is simple to implement and vectorizes very well. The algorithm was implemented on all three machines and tested by classifying the same full scene of LANDSAT multispectral scan data. Timings are compared as well as features of the machines and available software

Use of LANDSAT imagery for wildlife habitat mapping in northeast and east central Alaska

The author has identified the following significant results. Two scenes were analyzed by applying an iterative cluster analysis to a 2% random data sample and then using the resulting clusters as a training set basis for maximum likelihood classification. Twenty-six and twenty-seven categorical classes, respectively resulted from this process. The majority of classes in each case were quite specific vegetation types; each of these types has specific value as moose habitat

Urban sprawl analysis in Kutupalong Refugee Camp, Bangladesh

Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceUrban sprawling is a common phenomenon associated with geographical and political challenges such as refugee settlements and environmental extremes. Urban sprawl related to refugee or habitation settlement has been an area of active interest because of humanitarian and environmental problems. For example, higher rates of urban sprawling are positively correlated with higher rates of deforestation. The present study explored the viability and reproducibility of different classification techniques in assessing urban sprawl among Rohingya refugees in the Kutupalong refugee camp in South-Eastern Bangladesh. Two classification techniques were used to assess the urban sprawl among the study population. These classifications include the Support Vector Machine and Maximum Likelihood Classifier. The sprawl was measured based on the classification of urban ad non-urban classes, according to the topography of the camps. The study showed that urban class exhibited exponential growth from 2.01 km2 to 5.37 km2 within nine months based on Support Vector Machine Classifier, while Maximum Likelihood Classification detected 3.2 km2 to 7.8 km2 of urbanization. On the contrary, the non-urban class shrunk from 12.58 km2 to 9.95 km2 during the same period with Support Vector Machine and 11.3 km2 to 6.7 km2 with Maximum Likelihood Classification. The Support Vector Machine yielded better overall accuracy performance compared to Maximum Likelihood Classification