scholarly journals Assimilation of Standard Regularizer Contextual Model and Composite Kernel with Fuzzy-based Noise Classifier

2019 ◽  
Vol 11 (1) ◽  
pp. 16-24
Author(s):  
Ishuita SenGupta ◽  
Anil Kumar ◽  
Rakesh Kumar Dwivedi
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Classification Accuracy ◽  
Remote Sensing Data ◽  
Kernel Functions ◽  
Contextual Model ◽  
Markov Random ◽  
Composite Kernel ◽  
Kernel Composition ◽  
Standard Regularization

The paper assay the effect of assimilating smoothness prior contextual model and composite kernel function with fuzzy based noise classifier using remote sensing data. The concept of the composite kernel has been taken by fusing two kernels together to improve the classification accuracy. Gaussian and Sigmoid kernel functions have opted for kernel composition. As a contextual model, Markov Random Field (MRF) Standard regularization model (smoothness prior) has been studied with the composite kernel-based Noise Classifier. Comparative analysis of new classifier with the conventional construes increase in overall accuracy.

scholarly journals Lossy Compression of Multichannel Remote Sensing Images with Quality Control

2020 ◽  
Vol 12 (22) ◽  
pp. 3840
Author(s):  
Vladimir Lukin ◽  
Irina Vasilyeva ◽  
Sergey Krivenko ◽  
Fangfang Li ◽  
Sergey Abramov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Real Life ◽  
Remote Sensing Data ◽  
Original Data ◽  
Lossy Compression ◽  
Compressed Images ◽  
Training Methodology ◽  
Neural Network Classifiers

Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given.

Ensemble Learning on Hyperspectral Remote Sensing Image Classification

2012 ◽  
Vol 546-547 ◽  
pp. 508-513 ◽  
Author(s):  
Qiong Wu ◽  
Ling Wei Wang ◽  
Jia Wu
Keyword(s):  
Remote Sensing ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Remote Sensing Image Classification ◽  
Hyperspectral Remote Sensing Image

The characteristics of hyperspectral data with large number of bands, each bands have correlation, which has required a very high demand of solving the problem. In this paper, we take the features of hyperspectral remote sensing data and classification algorithms as the background, applying the ensemble learning to image classification.The experiment based on Weka. I compared the classification accuracy of Bagging, Boosting and Stacking on the base classifiers J48 and BP. The results show that ensemble learning on hyperspectral data can achieve higher classification accuracy. So that it provide a new method for the classification of hyperspectral remote sensing image.

scholarly journals Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy

2018 ◽  
Vol 10 (8) ◽  
pp. 1267 ◽  
Author(s):  
Natalia Verde ◽  
Giorgos Mallinis ◽  
Maria Tsakiri-Strati ◽  
Charalampos Georgiadis ◽  
Petros Patias
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Classification Tree ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Computational Time ◽  
Sensing Data ◽  
Road Map ◽  
Data Volume ◽  
The Impact

Improved sensor characteristics are generally assumed to increase the potential accuracy of image classification and information extraction from remote sensing imagery. However, the increase in data volume caused by these improvements raise challenges associated with the selection, storage, and processing of this data, and with the cost-effective and timely analysis of the remote sensing datasets. Previous research has extensively assessed the relevance and impact of spatial, spectral and temporal resolution of satellite data on classification accuracy, but little attention has been given to the impact of radiometric resolution. This study focuses on the role of radiometric resolution on classification accuracy of remote sensing data through different classification experiments over three different sites. The experiments were carried out using fine and low scale radiometric resolution images classified through a bagging classification tree. The classification experiments addressed different aspects of the classification road map, including among others, binary and multiclass classification schemes, spectrally and spatially enhanced images, as well as pixel and objects as units of the classification. In addition, the impact of image radiometric resolution on computational time and the information content in fine- and low-resolution images was also explored. While in certain cases, higher radiometric resolution has led to up to 8% higher classification accuracies compared to lower resolution radiometric data, other results indicate that higher radiometric resolution does not necessarily imply improved classification accuracy. Also, classification accuracy of spectral indices and texture bands is not related so much to the radiometric resolution of the original remote sensing images but rather to their own radiometric resolution. Overall, the results of this study suggest that data selection and classification need not always adhere to the highest possible radiometric resolution.

scholarly journals Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China

2020 ◽  
Vol 51 (5) ◽  
pp. 942-958 ◽  
Author(s):  
Jianzhu Li ◽  
Siyao Zhang ◽  
Lingmei Huang ◽  
Ting Zhang ◽  
Ping Feng
Keyword(s):  
Remote Sensing ◽  
Prediction Models ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Kernel Functions ◽  
Support Vector ◽  
Sensing Data ◽  
Model Driven ◽  
Drought Prediction ◽  
Svm Model

Abstract Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed the following. (1) The SVM model performed the best when the models were developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.

Influence of Clique Potential Parameters on Classification Using Bayesian MRF Model for Remote Sensing Image in Dali Erhai Basin

2013 ◽  
Vol 658 ◽  
pp. 508-512
Author(s):  
Hai Jun Duan ◽  
Guang Min Wu ◽  
Dan Liu ◽  
John D. Mai ◽  
Jian Ming Chen
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Contextual Information ◽  
Reference Value ◽  
Remote Sensing Data ◽  
Classification Model ◽  
Lake Basin ◽  
Erhai Lake ◽  
Mountain Areas ◽  
Potential Parameters

Image classification of remote sensing data is an important topic and long-term tasks in applications [1]. Markov random field (MRF) has more advantages in processing contextual information [2]. Bayesian approach enables the incorporation of prior model and likelihood distribution, this paper has formulated a Bayesian-MRF classification model based on MAP-ICM framework. It uses Potts model in label field and assume Gaussian distribution in observation field. According to maximum a posteriori (MAP) criterion, each new classified label can be obtained by the minimum of energy using Iterated Conditional Modes (ICM) algorithm. Finally, classification tasks are carried out by Bayesian-MRF classification model. Experimental results show that: (1) Clique potential parameters affect classification greatly. When it is 0.5, the classification accuracy reaches maximum with the best classification result for study area of Dali Erhai Lake basin using landsat TM data. (2) Bayesian MRF model have obvious advantages in classification for neighbourhood pixels so that it can separate Shadow class from Water class because the Shadow in mountain areas is very similar to Water in spectrum. In this case study, the best classification accuracy reaches 95.8%. The approaches and results will have important reference value for applications such as land use/cover classification, environment/ecological monitoring etc.

Sign in / Sign up

Export Citation Format

Share Document