scholarly journals CLASSIFICATION OF SATELLITE FUSED DATA FOR LAND USE MAPPING IN DEVELOPMENT PLAN

2011 ◽  
Vol 9 (2) ◽  
Author(s):  
Norzailawati Mohd Noor ◽  
Alias Abdullah ◽  
Mazlan Hashim
Keyword(s):  
Land Use ◽  
Principal Component ◽  
Kappa Statistic ◽  
Development Plan ◽  
Remotely Sensed Data ◽  
Data Set ◽  
Principal Component Transform ◽  
Fused Image ◽  
Fine Resolution

Land use mapping in development plan basically provides resources of information and important tool in decision making. In relation to this, fine resolution of recent satellite remotely sensed data have found wide applications in land use/land cover mapping. This study reports on work carried out for classification of fused image for land use mapping in detail scale for Local Plan. The LANDSATTM, SPOT Pan and IKONOS satellite were fused and examined using three data fusion techniques, namely Principal Component Transfonn (PCT), Wavelet Transform and Multiplicative fusing approach. The best fusion technique for three datasets was determined based on the assessment of class separabilities and visualizations evaluation of the selected subset of the fused datasets, respectively. Principal Component Transform has been found to be the best technique for fusing the three datasets, where the best fused data set was subjected to further classification for producing level of land use classes while level II and III pass on to nine classes of detail classification for local plan. The overall data classification accuracy of the best fused data set was 0.86 (kappa statistic). Final land use output from classified data was successfully generated in accordance to local plan land use mapping for development plan purposes.

CLASSIFICATION OF SATELLITE FUSED DATA FOR LAND USE MAPPING IN DEVELOPMENT PLAN

2011 ◽  
Vol 9 ◽  
Author(s):  
Noorzailawati Mohd Noor ◽  
Alias Abdullah ◽  
Mazlan Hashim
Keyword(s):  
Land Use ◽  
Principal Component ◽  
Kappa Statistic ◽  
Development Plan ◽  
Remotely Sensed Data ◽  
Data Set ◽  
Principal Component Transform ◽  
Fused Image ◽  
Fine Resolution

Land use mapping in development plan basically provides resources of information and important tool in decision making. In relation to this, fine resolution of recent satellite remotely sensed data have found wide applications in land use/land cover mapping. This study reports on work carried out for classification of fused image for land use mapping in detail scale for Local Plan. The LANDSATTM, SPOT Pan and IKONOS satellite were fused and examined using three data fusion techniques, namely Principal Component Transfonn (PCT), Wavelet Transform and Multiplicative fusing approach. The best fusion technique for three datasets was determined based on the assessment of class separabilities and visualizations evaluation of the selected subset of the fused datasets, respectively. Principal Component Transform has been found to be the best technique for fusing the three datasets, where the best fused data set was subjected to further classification for producing level of land use classes while level II and III pass on to nine classes of detail classification for local plan. The overall data classification accuracy of the best fused data set was 0.86 (kappa statistic). Final land use output from classified data was successfully generated in accordance to local plan land use mapping for development plan purposes.

scholarly journals An Optimal Approach for Land-Use / Land-Cover Mapping by Integration and Fusion of Multispectral Landsat OLI Images: Case Study in Baghdad, Iraq

2020 ◽  
Vol 231 (9) ◽  
Author(s):  
Hayder Dibs ◽  
Hashim Ali Hasab ◽  
Jawad K. Al-Rifaie ◽  
Nadhir Al-Ansari
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Confusion Matrix ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Accurate Estimation ◽  
Data Set ◽  
Fused Image ◽  
Spectral Algorithm ◽  
Optimal Approach

Abstract Using solely an optical remotely sensed dataset to obtain an accurate thematic map of land use and land cover (LU/LC) is a serious challenge. The dataset fusion of multispectral and panchromatic images play a big role and provide an accurate estimation of LU/LC map simply because using a dataset from different spectrum portions with different spatial and spectral characteristics will improve image classification. For this study, the Landsat operational land imager multispectral and panchromatic images were adopted. This study aimed to investigate the effectiveness of using a panchromatic highly spatial resolution to refine the methodology for LU/LC mapping in Baghdad city, Iraq, by performing a comparison of classifications using different algorithms on multispectral and fused images. Different classification algorithms were employed to classify the data set; minimum distance (MD) and the maximum likelihood classifier (MLC). A suitable classification method was proposed to map LU/LC based on the outcome results. The result evaluation was conducted by applying a confusion matrix. An overall accuracy of a fused image using a principal component-based spectral sharpening algorithm and classified by the MLC classifier reveals the highest accurate results with an overall accuracy and kappa coefficient of 98.90% and 0.98, respectively. Results showed that the best methodology for LU/LC mapping of the study area is found from fusion of multispectral with panchromatic images via principal component-based spectral algorithm with MLC approach for classification.

scholarly journals A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5097 ◽  
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Changes ◽  
Dimensional Space ◽  
Principal Component ◽  
Majority Voting ◽  
Data Set ◽  
Centroid Distance ◽  
Low Dimensional ◽  
Reduced Data

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method.

scholarly journals Mapping Soil Quality in Various Land Uses as a Basis for Soil Management in Wonogiri, Indonesia

2020 ◽  
Vol 11 (2) ◽  
pp. 127-135
Author(s):  
Mujiyo MUJIYO ◽  
Suntoro SUNTORO ◽  
Restu Prasetyaning TYAS ◽  
Aktavia HERAWATI ◽  
Hery WIDIJANTO
Keyword(s):  
Land Use ◽  
Soil Quality ◽  
Microbial Biomass Carbon ◽  
Principal Component ◽  
Soil Management ◽  
Agroforestry System ◽  
Land Uses ◽  
Soil Quality Index ◽  
Data Set ◽  
Quality Status

Soil quality is closely related to environment because soil is not only viewed as a growing media for plants but also encompasses various environmental and health functions. It is important to know the quality of soil in order to keep it healthy, productive, and optimally functioning. This research aims to evaluate soil quality status in various land uses and to learn the land factors that are related to soil quality. Soil quality index (SQI) represents the soil quality status. SQI will then be used as the basis for soil management. A descriptive explorative research study was carried out in the Giritontro Sub-district, Wonogiri District, Indonesia. SQI indicators were obtained from 12 existing Land Mapping Units (LMU). SQI was obtained by determining the Minimum Data Set (MDS) with a Principal Component Analysis (PCA) test. Then SQI was mapped and statistically analyzed to determine the influence of land use and the determinant factors of SQI. Results showed that SQI in all area is class 3 or moderate. SQI was significantly influenced by land use. SQI in paddy field is 9.09% higher than crop fields and 2.27% higher than of plantations. Indicators which are significantly related to SQI are bulk density, porosity, cation exchange capacity, available P, available K and microbial biomass carbon (MBC). The type of soil management that can be implemented to improve soil quality includes addition of organic or inorganic fertilizer and adoption of an agroforestry system.

scholarly journals LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 13-16 ◽  
Author(s):  
M. Cavur ◽  
H. S. Duzgun ◽  
S. Kemec ◽  
D. C. Demirkan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Remotely Sensed Data ◽  
Vector Machines ◽  
Kappa Value ◽  
Sentinel 2

<p><strong>Abstract.</strong> Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.</p>

scholarly journals Land Cover Classification of Multispectral Remotely Sensed Data Based on Channel Relative Spatial Pattern

2020 ◽  
Vol 9 (3) ◽  
pp. 1415-1421
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Spatial Pattern ◽  
Land Cover Classification ◽  
Urban Region ◽  
Significant Issue ◽  
Remotely Sensed Data

Land spread grouping of remotely detected pictures includes characterizing the satellite pictures into various land use/land spread classes, for example, water, urban region, crop land, backwoods and so on. To screen the ecological effects. Highlights like shading and surface assume a prevalent job in land spread grouping. Picking an appropriate shading space is a significant issue for shading picture order. The quality of various shading spaces, for example, RGB, HSV, LUV have been coordinated successfully to make sense of the element vector. In this paper, another Channel Relative Spatial Pattern (CRSP) is proposed for separating the surface highlights. The extricated highlights are prepared and tried with Random Forest (RF) classifier. Examinations were directed on IRS LISS IV datasets and the outcomes were assessed dependent on the disarray grid, characterization exactness and Kappa insights. The proposed surface example is additionally contrasted and the (LBP), (LDP) and (LTrP) surface techniques and the precision appraisal results have demonstrated excep

Image Quality Assessment for Fused Remote Sensing Imageries

2014 ◽  
Vol 71 (4) ◽  
Author(s):  
Mohd Nadzri Md Reba ◽  
Ong Juey C’uang
Keyword(s):  
Image Quality Assessment ◽  
Principal Component ◽  
High Accuracy ◽  
Fusion Image ◽  
Precise Information ◽  
Qualitative And Quantitative ◽  
Fused Image ◽  
Quantitative Assessments

Image fusion provides precise information in both spatial and spectral resolutions that benefit significantly in high accuracy mapping. Yet, there is less intention withdrawn in justifying the performance of the fused image. In this study, qualitative and quantitative assessments were carried out to test the quality of fusion image. Principal Component Analysis (PCA), Gram-Schmidt and Ehlers were applied to fuse the hyperspectral and Lidar image. Ehlers fusion showed good in preserving the color of image and contained the most information. Besides, the classification of Ehlers fused image showed the highest accuracy.

scholarly journals Pattern Recognition of Tobacco Headspace GC Profiles: A Potential New Analytical Tool for the Classification of Raw Tobaccos

1988 ◽  
Vol 14 (2) ◽  
pp. 93-103
Author(s):  
F Heinzer ◽  
HP Maitre ◽  
M Rigaux ◽  
J Wild
Keyword(s):  
Closed Loop ◽  
Capillary Column ◽  
Principal Component ◽  
Glass Capillary ◽  
Data Set ◽  
Data Points ◽  
Retention Characteristics ◽  
Chemometric Approach ◽  
Different Origins

AbstractThe first part of the paper describes a new method of obtaining reproducible and meaningful headspace profiles of tobacco lamina by using a modified closed loop stripping apparatus. The complex chromatograms are obtained by high-resolution glass capillary gas chromatography. The second part summarizes the results of a chemometric approach to interpret the chromatograms obtained from a series of nine Virginia flue-cured tobaccos from different origins and belonging to different quality groups, each one analysed three times by the method described above. After the elimination of peaks containing redundant information, the resulting data set, consisting of 27 × 17 data points, was analysed to detect natural groupings by using an in-house program (in Basic) for principal component analysis. A subsequent discriminant analysis yielded two discriminant functions capable of separating the nine Virginia tobaccos into three quality groups as defined by a conventional organoleptic analysis carried out by a smoking panel. All the tobaccos could be classified correctly (100 %). A first attempt to classify, by the procedure described above, a group of six Virginia tobaccos whose organoleptic scores were not known, did not yield clearly interpretable results, possibly because the performance of the capillary column used for analysis had slightly deteriorated during the experiment with resultant changes in retention characteristics, which led to wrong identifications of certain peaks.

scholarly journals Misclassification of Calcium Status Based on Albumin-Adjusted Calcium: Studies in a Tertiary Hospital Setting

2018 ◽  
Vol 64 (12) ◽  
pp. 1713-1722 ◽  
Author(s):  
Joel D Smith ◽  
Scott Wilson ◽  
Hans G Schneider
Keyword(s):  
Hospital Setting ◽  
Kappa Statistic ◽  
Tertiary Hospital ◽  
Data Sets ◽  
Data Set ◽  
Paired Samples ◽  
Correct Assignment ◽  
Hospital Patients ◽  
Calcium Status

Abstract BACKGROUND Clinical laboratories measure total calcium and adjust for albumin concentrations to predict calcium status. We compared total and adjusted calcium (Adj-Ca) with ionized calcium (Ca2+) for correct assignment of calcium status. The effect of restriction of Adj-Ca reporting in patients with hypoalbuminemia was determined on the basis of frequency of misclassifications. METHODS Extraction of laboratory results was performed for 24 months. Adj-Ca was calculated from a modified Payne formula. A further prospective data set for 6 months was collected after stopping reporting of Adj-Ca for patients with an albumin &lt;3.0 g/dL. The agreement between Ca2+ and Adj-Ca or total Ca was assessed with Cohen's kappa statistic. RESULTS In 5553 hospitalized patients, 13604 paired Ca2+ results were analyzed retrospectively. Prospective collection in 1113 paired samples was from 450 patients. Adj-Ca was a poor predictor of calcium status compared to the Ca2+ reference standard in both data sets (agreement 56.9% in the first, 65.6% in the second data set). Renal failure and low albumin concentrations were associated with worse agreement between Adj-Ca and Ca2+. Restriction of reporting of Adj-Ca to albumin concentrations &gt;3.0g/dL improved correct classification of calcium status from 65.6% to 77.6% (P &lt; 0.0001). Total Ca performed better than Adj-Ca for low albumin (&lt;3.0g/dL) and performed similarly in samples with albumin &gt;3.0g/dL. CONCLUSIONS Adj-Ca is unreliable for the classification of calcium status in hospital patients when compared to Ca2+. Adj-Ca overestimates calcium for patients with renal impairment and albumin concentrations &lt;3.0g/dL. Restriction of reporting Adj-Ca for albumin below 3.0 g/dL reduces the number of misclassified patients.

scholarly journals Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning

2019 ◽  
Vol 11 (22) ◽  
pp. 2690 ◽  
Author(s):  
Yushi Chen ◽  
Lingbo Huang ◽  
Lin Zhu ◽  
Naoto Yokoya ◽  
Xiuping Jia
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Conditional Random Field ◽  
Hyperspectral Imagery ◽  
Principal Component ◽  
Processing Technique ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Fine Grained

Hyperspectral remote sensing obtains abundant spectral and spatial information of the observed object simultaneously. It is an opportunity to classify hyperspectral imagery (HSI) with a fine-grained manner. In this study, the fine-grained classification of HSI, which contains a large number of classes, is investigated. On one hand, traditional classification methods cannot handle fine-grained classification of HSI well; on the other hand, deep learning methods have shown their powerfulness in fine-grained classification. So, in this paper, deep learning is explored for HSI supervised and semi-supervised fine-grained classification. For supervised HSI fine-grained classification, densely connected convolutional neural network (DenseNet) is explored for accurate classification. Moreover, DenseNet is combined with pre-processing technique (i.e., principal component analysis or auto-encoder) or post-processing technique (i.e., conditional random field) to further improve classification performance. For semi-supervised HSI fine-grained classification, a generative adversarial network (GAN), which includes a discriminative CNN and a generative CNN, is carefully designed. The GAN fully uses the labeled and unlabeled samples to improve classification accuracy. The proposed methods were tested on the Indian Pines data set, which contains 33,3951 samples with 52 classes. The experimental results show that the deep learning-based methods provide great improvements compared with other traditional methods, which demonstrate that deep models have huge potential for HSI fine-grained classification.

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