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Using a GEOBIA framework for integrating different data sources and classification methods in context of land use/land cover mapping

Anna Osmólska Paweł Hawryło
Geodesy and Cartography | 2018 | vol. 67 | No 1
Keywords data fusion random forest supervised classification Sentinel-2
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Abstract

Land use/land cover (LULC) maps are important datasets in various environmental projects. Our aim was to demonstrate how GEOBIA framework can be used for integrating different data sources and classification methods in context of LULC mapping.We presented multi-stage semi-automated GEOBIA classification workflow created for LULC mapping of Tuszyma Forestry Management area based on multi-source, multi-temporal and multi-resolution input data, such as 4 bands- aerial orthophoto, LiDAR-derived nDSM, Sentinel-2 multispectral satellite images and ancillary vector data. Various classification methods were applied, i.e. rule-based and Random Forest supervised classification. This approach allowed us to focus on classification of each class ‘individually’ by taking advantage from all useful information from various input data, expert knowledge, and advanced machine-learning tools. In the first step, twelve classes were assigned in two-steps rule-based classification approach either vector-based, ortho- and vector-based or orthoand Lidar-based. Then, supervised classification was performed with use of Random Forest algorithm. Three agriculture-related LULC classes with vegetation alternating conditions were assigned based on aerial orthophoto and Sentinel-2 information. For classification of 15 LULC classes we obtained 81.3% overall accuracy and kappa coefficient of 0.78. The visual evaluation and class coverage comparison showed that the generated LULC layer differs from the existing land cover maps especially in relative cover of agriculture-related classes. Generally, the created map can be considered as superior to the existing data in terms of the level of details and correspondence to actual environmental and vegetation conditions that can be observed in RS images.
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Authors and Affiliations

Anna Osmólska
Paweł Hawryło

Fall Detector Using Discrete Wavelet Decomposition And SVM Classifier

Bartłomiej Wójtowicz Andrzej Dobrowolski Kazimierz Tomczykiewicz
Metrology and Measurement Systems | 2015 | vol. 22 | No 2 | 303-314 | DOI: 10.1515/mms-2015-0026
Keywords fall detection discrete wavelet transform data fusion support vector machine
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Abstract

This paper presents the design process and the results of a novel fall detector designed and constructed at the Faculty of Electronics, Military University of Technology. High sensitivity and low false alarm rates were achieved by using four independent sensors of varying physical quantities and sophisticated methods of signal processing and data mining. The manuscript discusses the study background, hardware development, alternative algorithms used for the sensor data processing and fusion for identification of the most efficient solution and the final results from testing the Android application on smartphone. The test was performed in four 6-h sessions (two sessions with female participants at the age of 28 years, one session with male participants aged 28 years and one involving a man at the age of 49 years) and showed correct detection of all 40 simulated falls with only three false alarms. Our results confirmed the sensitivity of the proposed algorithm to be 100% with a nominal false alarm rate (one false alarm per 8 h).
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Authors and Affiliations

Bartłomiej Wójtowicz
Andrzej Dobrowolski
Kazimierz Tomczykiewicz

Information fusion method of multichannel nanosensors based on neural network

Chaoke Li
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 2 | e140258 | DOI: 10.24425/bpasts.2022.140258
Keywords nanosensors multiplexing information fusion data fusion neural network
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Abstract

Information fusion approaches have been commonly used in multi sensor environments for the fusion and grouping of data from various sensors which is used further to draw a meaningful interpretation of the data. Traditional information fusion methods have limitations such as high time complexity of fusion processes and poor recall rate. In this work, a new multi-channel nano sensor information fusion method based on a neural network has been designed. By analyzing the principles of information fusion methods, the back propagation based neural network (BP-NN) is devised in this work. Based on the design of the relevant algorithm flow, information is collected, processed, and normalized. Then the algorithm is trained, and output is generated to achieve the fusion of information based on multi-channel nano sensor. Moreover, an error function is utilized to reduce the fusion error. The results of the present study show that compared with the conventional methods, the proposed method has quicker fusion (integration of relevant data) and has a higher recall rate. The results indicate that this method has higher efficiency and reliability. The proposed method can be applied in many applications to integrate the data for further analysis and interpretations.
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Authors and Affiliations

Chaoke Li
1
  1. School of Intelligent Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang 453003, China

Alternative Automatic Vehicle Classification Method

Piotr Burnos
Metrology and Measurement Systems | 2010 | No 3 | 323-332 | DOI: 10.2478/v10178-010-0028-2
Keywords automatic vehicle classification ITS weigh-in-motion WIM fuzzy sets data fusion
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Abstract

The paper deals with the new method of automatic vehicle classification called ALT (ALTernative). Its characteristic feature is versatility resulting from its open structure, moreover a user can adjust the number of vehicles and their category according to individual requirements. It uses an algorithm for automatic vehicle recognition employing data fusion methods and fuzzy sets. High effectiveness of classification while retaining high selectivity of division was proved by test results. The effectiveness of classification of all vehicles at the level of 95% and goods trucks of 100% is more than satisfactory.

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Authors and Affiliations

Piotr Burnos

Fusion of Data from Inertial Sensors, Raster Maps and GPS for Estimation of Pedestrian Geographic Location in Urban Terrain

Przemysław Barański Maciej Polańczyk Pawel Strumillo
Metrology and Measurement Systems | 2011 | No 1 | 145-158 | DOI: 10.2478/v10178-011-0014-3
Keywords pedestrian navigation data fusion GPS digital maps particle filtering electronic travel aids
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Abstract

An electronic system and an algorithm for estimating pedestrian geographic location in urban terrain is reported in the paper. Different sources of kinematic and positioning data are acquired (i.e.: accelerometer, gyroscope, GPS receiver, raster maps of terrain) and jointly processed by a Monte-Carlo simulation algorithm based on the particle filtering scheme. These data are processed and fused to estimate the most probable geographical location of the user. A prototype system was designed, built and tested with a view to aiding blind pedestrians. It was shown in the conducted field trials that the method yields superior results to sole GPS readouts. Moreover, the estimated location of the user can be effectively sustained when GPS fixes are not available (e.g. tunnels).

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Authors and Affiliations

Przemysław Barański
Maciej Polańczyk
Pawel Strumillo

Fast multispectral deep fusion networks

V. Osin A. Cichocki E. Burnaev
Bulletin of the Polish Academy of Sciences Technical Sciences | 2018 | 66 | No 6 (Special Section on Deep Learning: Theory and Practice) | 875-889 | DOI: 10.24425/bpas.2018.125935
Keywords multi-spectral imaging data fusion deep learning convolutional networks object detection image segmentation
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Authors and Affiliations

V. Osin
A. Cichocki
E. Burnaev

Measurements of the Methane Concentration Along the Longwall Excavations and Longwall

Piotr Ostrogórski Przemysław Skotniczny Mieczysław Pucka
Archives of Mining Sciences | 2022 | vol. 67 | No 1 | 195-204 | DOI: 10.24425/ams.2022.140861
Keywords measurements of the methane concentration section measurements Y system individual methane meters inspection and monitoring mining data fusion
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Abstract

One of the most hazardous places in mines are longwall areas. They emit a considerable amount of methane to the ventilation air. The emission depends on many but mostly known factors. The article presents the research results on changes in the methane concentration along the longwall excavations and longwall. The distributions were obtained based on a measurement experiment at the ZG Brzeszcze mine in Poland. The author’s research aimed to experimentally determine the concentration of methane as a function of the length of excavation for the longwall excavations and longwall. As a result, methane concentration trends along the excavations were obtained. The conclusions show the pros and cons of the method used, and it allows to set the right direction in the development of measurement systems and sensors.
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Authors and Affiliations

Piotr Ostrogórski
1
ORCID: ORCID
Przemysław Skotniczny
1
ORCID: ORCID
Mieczysław Pucka
2
  1. Strata Mechanics Institute, Polish Academy of Sciences, 27 Reymonta Str., 30-059 Kraków, Poland
  2. Tauron Wydobycie S.A. ZG Brzeszcze, ul. Kościuszki 1, 32-620 Brzeszcze, Poland

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