IEEE GRSS Data Fusion Contest Research Articles

Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest

This paper presents the scientific outcomes of the 2018 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2018 Contest addressed the problem of urban observation and monitoring with advanced multi-source optical remote sensing (multispectral LiDAR, hyperspectral imaging, and very high-resolution imagery). The competition was based on urban land use and land cover classification, aiming to distinguish between very diverse and detailed classes of urban objects, materials, and vegetation. Besides data fusion, it also quantified the respective assets of the novel sensors used to collect the data. Participants proposed elaborate approaches rooted in remote-sensing, and also in machine learning and computer vision, to make the most of the available data. Winning approaches combine convolutional neural networks with subtle earth-observation data scientist expertise.

Deep learning decision fusion for the classification of urban remote sensing data

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral–spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area

ABSTRACTMulti-sensor data fusion has become more and more popular for classification applications. The fusion of multisource remote-sensing data can provide more information about the same observed site results in a superior comprehension of the scene. In this field of study, a combination of very high-resolution data collected by a digital color camera and a new coarse resolution hyperspectral data in the long-wave infrared range for urban land-cover classification has been extensively enticed much consideration and turned into a research hot spot in image analysis and data fusion research community. In this paper, a decision-based multi-sensor classification system is proposed to completely use the advantages of both sensors to attain enhanced land-cover classification results. In this context, spectral, textural and spatial features are extracted for the proposed multilevel classification. Then, a land-cover separability preprocessing is employed to identify how the proposed method can fully utilize the sensor advantages. Next, a support vector machine is applied to classify road classes by using thermal hyperspectral image data; plants, roofs and bare soils are classified by the joint use of sensors via Dempster–Shafer classifier fusion. Finally, an object-based post-processing is employed to improve the classification results. Experiments carried out on the dataset of 2014 IEEE GRSS data fusion contest indicate the superiority of the proposed methodology for the potentialities and possibilities of the joint utilization of sensors and refine the classification outcomes when evaluated against single sensor data. Meanwhile, the obtained classification accuracy can be a competitor against the results issued by the 2014 IEEE GRSS data fusion contest.

Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest

In this paper, we discuss the scientific outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high-resolution RGB images and a three-dimensional (3-D) LiDAR point cloud. The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this paper, we discuss the scientific results obtained by the winners of the 2-D contest, which studied either the complementarity of RGB and LiDAR with deep neural networks (winning team) or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data (runner-up team). The data and the previously undisclosed ground truth will remain available for the community and can be obtained at http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest/. The 3-D part of the contest is discussed in the Part-B paper [1].

Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest

Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest

Classification of Multisensor Images with Different Spatial Resolution

The paper is focused on the analysis of classification possibilities of multisensor data with different spatial resolutions using combined classifiers based on Bayes approach with equal prior probabilities and on minimum of the Mahalanobis distance. The task set up for the 2014 IEEE GRSS Data Fusion Contest was chosen as an application example. High resolution RGB image and lower resolution thermal infrared image from the same urban area were processed to perform classification of each higher resolution pixel. Development of a fast and straightforward procedure was targeted and combined classifiers are proposed for that, exploiting spectral features from each data set separately. It is shown that data fusion can be achieved using the proposed classifiers and improvement of classification quality can be obtained with respect to the cases where only one of the data sets is used. The best classification results were obtained using the combined Bayes- type classifier that provided overall classification accuracy of about 95 % when the ground truth pixels from the high resolution RGB image were used both for design and testing. DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13333

Classification of Multisensor Images with Different Spatial Resolution

The paper is focused on the analysis of classification possibilities of multisensor data with different spatial resolutions using combined classifiers based on Bayes approach with equal prior probabilities and on minimum of the Mahalanobis distance. The task set up for the 2014 IEEE GRSS Data Fusion Contest was chosen as an application example. High resolution RGB image and lower resolution thermal infrared image from the same urban area were processed to perform classification of each higher resolution pixel. Development of a fast and straightforward procedure was targeted and combined classifiers are proposed for that, exploiting spectral features from each data set separately. It is shown that data fusion can be achieved using the proposed classifiers and improvement of classification quality can be obtained with respect to the cases where only one of the data sets is used. The best classification results were obtained using the combined Bayes- type classifier that provided overall classification accuracy of about 95 % when the ground truth pixels from the high resolution RGB image were used both for design and testing.DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13333

Synergetic Classification of Long-Wave Infrared Hyperspectral and Visible Images

A decision-level-based synergetic classification method has been proposed to conduct land-cover classification for long-wave infrared (LWIR) and high-resolution visible (VIS) images in this paper. The problem of synergic classification is challenging, since we aim for classification map at the spatial resolution of the VIS image under different imaging modes. The proposed method consists of two stages, i.e., stage of classifications for LWIR hyperspectral (HS) and VIS images separately, and stage of decision-level fusion for both classification results. For LWIR HS image, we have proposed a new semisupervised feature extraction method named as semisupervised local discriminant analysis (SLDA) for SVM classification. In parallel, spatial features which have been extracted from the high-resolution VIS image are used to combine with the spectral features for classification. In the second stage, several common decision-level fusion rules have been employed to integrate both classification results. We also present a context-based opinion pools (CBP) strategy to enhance the classification accuracy. Experiments conducted on the dataset of 2014 IEEE GRSS Data Fusion Contest show the advantage of our proposed SLDA method for HS image, and the effect of spatial–spectral features for high-resolution VIS image. Especially, the presented synergic classification strategy for HS and VIS images has higher overall accuracy and better visual effect than those only using single source image and those compared fusion methods in the experiments.

Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest

This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multisensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.

Report on the 2014 IEEE GRSS Data Fusion Contest: Multiresolution Fusion of Thermal Hyperspectral and VIS Data [Technical Committees

Each year since 2006, the Image Analysis and Data Fusion Technical Committee (IADF TC, formerly the Data Fusion Technical Committee) of the Geoscience and Remote Sensing Society organizes a data fusion contest aiming at evaluating existing methodologies and fostering new ideas at the operational and research level to solve multisource remote sensing problems [1-7].

Fusion of Hyperspectral and LiDAR Data for Landscape Visual Quality Assessment

Landscape visual quality is an important factor associated with daily experiences and influences our quality of life. In this work, the authors present a method of fusing airborne hyperspectral and mapping light detection and ranging (LiDAR) data for landscape visual quality assessment. From the fused hyperspectral and LiDAR data, classification and depth images at any location can be obtained, enabling physical features such as land-cover properties and openness to be quantified. The relationship between physical features and human landscape preferences is learned using least absolute shrinkage and selection operator (LASSO) regression. The proposed method is applied to the hyperspectral and LiDAR datasets provided for the 2013 IEEE GRSS Data Fusion Contest. The results showed that the proposed method successfully learned a human perception model that enables the prediction of landscape visual quality at any viewpoint for a given demographic used for training. This work is expected to contribute to automatic landscape assessment and optimal spatial planning using remote sensing data.

2014 IEEE GRSS Data Fusion Contest: Multiresolution Fusion of Thermal Hyperspectral and VIS Data [Technical Committees

2014 IEEE GRSS Data Fusion Contest: Multiresolution Fusion of Thermal Hyperspectral and VIS Data [Technical Committees