scholarly journals Semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks

2021 ◽  
pp. 103251
Author(s):  
Bodhiswatta Chatterjee ◽  
Charalambos Poullis
Keyword(s):  
Semantic Segmentation ◽  
Sensor Data ◽  
Latent Learning ◽  
Remote Sensor

scholarly journals An Investigation on Land Cover Mapping Capability of Classical and Fuzzy based Maximum Likelihood Classifiers

2018 ◽  
Vol 7 (2) ◽  
pp. 939 ◽  
Author(s):  
Shivakumar B R ◽  
Rajashekararadhya S V
Keyword(s):  
Maximum Likelihood ◽  
Land Cover ◽  
Classification Accuracy ◽  
Distance Measure ◽  
Accuracy Assessment ◽  
Sensor Data ◽  
Remote Sensor ◽  
Mixed Pixels ◽  
Overlapping Classes

In the past two decades, a significant amount of research has been conducted in the area of information extraction from heterogeneous remotely sensed (RS) datasets. However, it is arduous to exactly predict the behaviour of the classification technique employed due to issues such as the type of the dataset, resolution of the imagery, the presence of mixed pixels, and spectrally overlapping of classes. In this paper, land cover classification of the heterogeneous dataset using classical and Fuzzy based Maximum Likelihood Classifiers (MLC) is presented and compared. Three decision parameters and their significance in pixel assignment is illustrated. The presented Fuzzy based MLC uses a weighted inverse distance measure for defuzzification process. 10 pixels were randomly selected from the study area to illustrate pixel assignment for both the classifiers. The study aims at enhancing the classification accuracy of heterogeneous multispectral remote sensor data characterized by spectrally overlapping classes and mixed pixels. The study additionally aims at obtaining classification results with a confidence level of 95% with ±4% error margin. Classification success rate was analysed using accuracy assessment. Fuzzy based MLC produced significantly higher classification accuracy as compared to classical MLC. The conducted research achieves the expected classification accuracy and proves to be a valuable technique for classification of heterogeneous RS multispectral imagery. 

scholarly journals A Novel Building Temperature Simulation Approach Driven by Expanding Semantic Segmentation Training Datasets with Synthetic Aerial Thermal Images

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 353
Author(s):  
Yu Hou ◽  
Rebekka Volk ◽  
Lucio Soibelman
Keyword(s):  
Semantic Segmentation ◽  
Sensor Data ◽  
University Campus ◽  
City Center ◽  
Thermal Images ◽  
Complex Envelope ◽  
Virtual Camera ◽  
Image Translation ◽  
Rgb Images ◽  
Synthetic Datasets

Multi-sensor imagery data has been used by researchers for the image semantic segmentation of buildings and outdoor scenes. Due to multi-sensor data hunger, researchers have implemented many simulation approaches to create synthetic datasets, and they have also synthesized thermal images because such thermal information can potentially improve segmentation accuracy. However, current approaches are mostly based on the laws of physics and are limited to geometric models’ level of detail (LOD), which describes the overall planning or modeling state. Another issue in current physics-based approaches is that thermal images cannot be aligned to RGB images because the configurations of a virtual camera used for rendering thermal images are difficult to synchronize with the configurations of a real camera used for capturing RGB images, which is important for segmentation. In this study, we propose an image translation approach to directly convert RGB images to simulated thermal images for expanding segmentation datasets. We aim to investigate the benefits of using an image translation approach for generating synthetic aerial thermal images and compare those approaches with physics-based approaches. Our datasets for generating thermal images are from a city center and a university campus in Karlsruhe, Germany. We found that using the generating model established by the city center to generate thermal images for campus datasets performed better than using the latter to generate thermal images for the former. We also found that using a generating model established by one building style to generate thermal images for datasets with the same building styles performed well. Therefore, we suggest using training datasets with richer and more diverse building architectural information, more complex envelope structures, and similar building styles to testing datasets for an image translation approach.

scholarly journals An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

2021 ◽  
Vol 13 (15) ◽  
pp. 3021
Author(s):  
Bufan Zhao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Weixing Xue ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Critical Data ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Cluster Optimization ◽  
Urban Scene ◽  
Processing Steps

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

scholarly journals Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage

2021 ◽  
Vol 185 ◽  
pp. 282-291
Author(s):  
Nizam U. Ahamed ◽  
Kellen T. Krajewski ◽  
Camille C. Johnson ◽  
Adam J. Sterczala ◽  
Julie P. Greeves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Sensor ◽  
Load Carriage ◽  
Sensor Data ◽  
Gait Patterns

Application of a testbed for validating remote sensor data and retrieval algorithms

2004 ◽  
Author(s):  
Alan E. Lipton ◽  
Jean-Luc Moncet ◽  
John Galantowicz ◽  
Haijun Hu ◽  
Richard Lynch ◽  
...  
Keyword(s):  
Sensor Data ◽  
Remote Sensor ◽  
Retrieval Algorithms

scholarly journals Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

Sensors ◽  
2017 ◽  
Vol 18 (2) ◽  
pp. 75 ◽  
Author(s):  
Ole Rindal ◽  
Trine Seeberg ◽  
Johannes Tjønnås ◽  
Pål Haugnes ◽  
Øyvind Sandbakk
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Automatic Classification ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Micro Sensor ◽  
Cross Country Skiing ◽  
Cross Country ◽  
Classical Cross

Robust Classification of "Artificial Nose" Sensor Data by Artificial Neural Networks

1997 ◽  
Vol 30 (9) ◽  
pp. 347-351 ◽  
Author(s):  
Z. Boger ◽  
L. Ratton ◽  
T.A. Kunt ◽  
T.J. Mc Avoy ◽  
R.E. Cavicchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sensor Data ◽  
Robust Classification ◽  
Artificial Nose ◽  
Artificial Neural
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