scholarly journals CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1061-1066 ◽  
Author(s):  
Amit Kumar Verma ◽  
P. K. Garg ◽  
K. S. Hari Prasad ◽  
V. K. Dadhwal
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Vegetation Indices ◽  
Spectral Information ◽  
Main Concern ◽  
Crop Land ◽  
Sensing Applications ◽  
Crop Classification ◽  
Decision Tree Method ◽  
Tree Method

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

scholarly journals CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1061-1066 ◽  
Author(s):  
Amit Kumar Verma ◽  
P. K. Garg ◽  
K. S. Hari Prasad ◽  
V. K. Dadhwal
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Vegetation Indices ◽  
Spectral Information ◽  
Main Concern ◽  
Crop Land ◽  
Sensing Applications ◽  
Crop Classification ◽  
Decision Tree Method ◽  
Tree Method

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

scholarly journals Classification of Landsat-8 Imagery Based On Pca And Ndvi Methods

2019 ◽  
Vol 8 (10) ◽  
pp. 4321-4325
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Region Of Interest ◽  
The Other ◽  
Spectral Information ◽  
Landsat 8 ◽  
Statistical Parameters ◽  
Sensing Applications

Remote sensing is an important issue in satellite image classification. In developing a significant sustainable system in agriculture farming, the major concern for remote sensing applications is the crop classification mechanism. The other important application in remote sensing is urban classification which gives the information about houses, roads, buildings, vegetation etc. A superior indicator for the presence of vegetation can be computed from the vegetation indices of a satellite image. This indicator supports in describing the health of vegetation through the image attributes like greenness and density. The other parameter in detecting objects or region of interest is an image is the texture. A satellite image contains spectral information and can be represented by more spectral bands and classification is very tough task. Generally, Classification of individual pixels in satellite images is based on the spectral information. In this research paper Principle component analysis and combination of PCA and NDVI classification methods are applied on Landsat-8 images. These images are acquired from USGS. The performance of these methods is compared in statistical parameters such as Kappa coefficient, overall accuracy, user’s accuracy, precision accuracy and F1 accuracy. In this work existing method is PCA and proposed method is PCA+NDVI. Experimental results shows that the proposed method has better statistical values compared to existing method.

scholarly journals Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil

2018 ◽  
pp. 55 ◽  
Author(s):  
J. P. D. Simioni ◽  
L. A. Guasselli ◽  
L. F. C. Ruiz ◽  
V. F. Nascimento ◽  
G. De Oliveira
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Oscillation Period ◽  
Remote Sensors ◽  
Port Facilities ◽  
Level Oscillation ◽  
Decision Tree Method ◽  
Sentinel 2A ◽  
Water Level Oscillation ◽  
Tree Method

<p>Vast small inner marsh (SIM) areas have been lost in the past few decades through the conversion to agricultural, urban and industrial lands. The remaining marshes face several threats such as drainage for agriculture, construction of roads and port facilities, waste disposal, among others. This study integrates 17 remote sensing spectral indexes and decision tree (DT) method to map SIM areas using Sentinel 2A images from Summer and Winter seasons. Our results showed that remote sensing indexes, although not developed specifically for wetland delimitation, presented satisfactory results in order to classify these ecosystems. The indexes that showed to be more useful for marshes classification by DT techniques in the study area were NDTI, BI, NDPI and BI_2, with 25.9%, 17.7%, 11.1% and 0.8%, respectively. In general, the Proportion Correct (PC) found was 95.9% and 77.9% for the Summer and Winter images respectively. We hypothetize that this significant PC variation is related to the rice-planting period in the Summer and/or to the water level oscillation period in the Winter. For future studies, we recommend the use of active remote sensors (e.g., radar) and soil maps in addition to the remote sensing spectral indexes in order to obtain better results in the delimitation of small inner marsh areas.</p>

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree

2013 ◽  
Vol 774-776 ◽  
pp. 1757-1761
Author(s):  
Bing Xiang Liu ◽  
Xu Dong Wu ◽  
Ying Xi Li ◽  
Xie Wei Wang
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Bp Neural Network ◽  
Cable Television ◽  
Model Building ◽  
Television System ◽  
Marketing Model ◽  
Television Service ◽  
Decision Tree Method ◽  
Tree Method

This paper takes more than four hundred records of some cable television system for example, makes data mining according to users data record, uses BP neural network and decision tree method respectively to have model building and finds the best model fits for users to order press service. The results of the experiment validate the methods feasibility and validity.

Sign in / Sign up

Export Citation Format

Share Document