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

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.

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Analysis of Surface Information Extraction of Erhai Lake Basin in Dali City Based on Remote Sensing Technology, 2005 - 2017

Proceedings of the 2nd International Conference on Intelligent Information Processing - IIP'17 ◽

10.1145/3144789.3144819 ◽

2017 ◽

Author(s):

Quan-Li Xu ◽

Chao Guo ◽

Wen Dong

Keyword(s):

Remote Sensing ◽

Information Extraction ◽

Lake Basin ◽

Erhai Lake ◽

Surface Information ◽

Remote Sensing Technology ◽

Sensing Technology

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Analyses of the Prądnik riverbed Shape Based on Archival and Contemporary Data Sets—Old Maps, LiDAR, DTMs, Orthophotomaps and Cross-Sectional Profile Measurements

Remote Sensing ◽

10.3390/rs12142208 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2208 ◽

Cited By ~ 1

Author(s):

Stanisław Szombara ◽

Paulina Lewińska ◽

Anna Żądło ◽

Marta Róg ◽

Kamil Maciuk

Keyword(s):

Remote Sensing ◽

Survey Methods ◽

Remote Sensing Data ◽

Sufficient Information ◽

Data Sets ◽

Time Step ◽

Mountain Areas ◽

Cross Sectional ◽

Sensing Data ◽

Old Maps

Analyses of riverbed shape evolution are crucial for environmental protection and local water management. For narrow rivers located in forested, mountain areas, it is difficult to use remote sensing data used for large river regions. We performed a study of the Prądnik River, located in the Ojców National Park (ONP), Poland. A multitemporal analysis of various data sets was performed. Light detection and ranging (LiDAR)-based data and orthophotomaps were compared with classical survey methods, and 78 cross-sectional profiles were done via GNSS and tachymetry. In order to add an extra time step, the old maps of this region were gathered, and their content was compared with contemporary data. The analysis of remote sensing data suggests that they do not provide sufficient information on the state and changes of riverbanks, river course or river depth. LiDAR data sets do not show river bottoms, and, due to plant life, do not document riverbanks. The orthophotomaps, due to tree coverage and shades, cannot be used for tracking the whole river course. The quality of old maps allows only for general shape analysis over time. This paper shows that traditional survey methods provide sufficient accuracy for such analysis, and the resulted cross-sectional profiles can and should be used to validate other, remote sensing, data sets. We diagnosed problems with the inventory and monitoring of such objects and proposed methods to refine the data acquisition.

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Quantitative analysis of land use and land cover changes from the multi-temporal remote sensing data in the Bosten Lake Basin, Chinese Tian Shan

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/74/1/012011 ◽

2017 ◽

Vol 74 ◽

pp. 012011

Author(s):

A Maimaiti ◽

L M Wang ◽

F Yan ◽

J Zhang ◽

Y X Ma

Keyword(s):

Remote Sensing ◽

Land Use ◽

Quantitative Analysis ◽

Land Cover ◽

Remote Sensing Data ◽

Land Cover Changes ◽

Lake Basin ◽

Bosten Lake ◽

Sensing Data ◽

Multi Temporal

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Lossy Compression of Multichannel Remote Sensing Images with Quality Control

Remote Sensing ◽

10.3390/rs12223840 ◽

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.

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Ensemble Learning on Hyperspectral Remote Sensing Image Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.546-547.508 ◽

2012 ◽

Vol 546-547 ◽

pp. 508-513 ◽

Cited By ~ 4

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.

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Wetland mapping in the Balqash Lake Basin Using Multi-source Remote Sensing Data and Topographic features Synergic Retrieval

Procedia Environmental Sciences ◽

10.1016/j.proenv.2011.09.422 ◽

2011 ◽

Vol 10 ◽

pp. 2718-2724 ◽

Cited By ~ 5

Author(s):

Changming Zhu ◽

Jiancheng Luo ◽

Zhanfeng Shen ◽

Chudong Huang

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Lake Basin ◽

Wetland Mapping ◽

Sensing Data ◽

Topographic Features

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A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area

Applied Sciences ◽

10.3390/app10082928 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2928 ◽

Cited By ~ 2

Author(s):

Rui Zhang ◽

Xinming Tang ◽

Shucheng You ◽

Kaifeng Duan ◽

Haiyan Xiang ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Remote Sensing Data ◽

Land Use Land Cover ◽

Mountain Areas ◽

Feature Level Fusion ◽

Artificial Surfaces ◽

Fusion Framework ◽

Level Fusion

Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.

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Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy

Remote Sensing ◽

10.3390/rs10081267 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1267 ◽

Cited By ~ 6

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.

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On the accuracy of glacier outlines derived from remote-sensing data

Annals of Glaciology ◽

10.3189/2013aog63a296 ◽

2013 ◽

Vol 54 (63) ◽

pp. 171-182 ◽

Cited By ~ 278

Author(s):

F. Paul ◽

N.E. Barrand ◽

S. Baumann ◽

E. Berthier ◽

T. Bolch ◽

...

Keyword(s):

Remote Sensing ◽

Reference Value ◽

Remote Sensing Data ◽

Automated Classification ◽

Automated Mapping ◽

Sensing Data ◽

Medium Resolution ◽

Debris Cover ◽

Manual Correction

AbstractDeriving glacier outlines from satellite data has become increasingly popular in the past decade. In particular when glacier outlines are used as a base for change assessment, it is important to know how accurate they are. Calculating the accuracy correctly is challenging, as appropriate reference data (e.g. from higher-resolution sensors) are seldom available. Moreover, after the required manual correction of the raw outlines (e.g. for debris cover), such a comparison would only reveal the accuracy of the analyst rather than of the algorithm applied. Here we compare outlines for clean and debris-covered glaciers, as derived from single and multiple digitizing by different or the same analysts on very high- (1 m) and medium-resolution (30 m) remote-sensing data, against each other and to glacier outlines derived from automated classification of Landsat Thematic Mapper data. Results show a high variability in the interpretation of debris-covered glacier parts, largely independent of the spatial resolution (area differences were up to 30%), and an overall good agreement for clean ice with sufficient contrast to the surrounding terrain (differences ∼5%). The differences of the automatically derived outlines from a reference value are as small as the standard deviation of the manual digitizations from several analysts. Based on these results, we conclude that automated mapping of clean ice is preferable to manual digitization and recommend using the latter method only for required corrections of incorrectly mapped glacier parts (e.g. debris cover, shadow).

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