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168 results on '"Rasti, Behnood"'

54. Unsupervised data fusion with deeper perspective : a novel multi-sensor deep clustering algorithm

Author

Rafiezadehshahi, Kasra, Ghamisi, Pedram, Rasti, Behnood, Scheunders, Paul, Gloaguen, Richard, and Shahi, Kasra Rafiezadeh

Subjects
convolutional autoencoder (CAE), Atmospheric Science, QC801-809, Economics, Physics, Geophysics. Cosmic physics, deep learning, Ocean engineering, remote sensing, Autoencoder (AE), multisensor data fusion, Computers in Earth Sciences, TC1501-1800, Engineering sciences. Technology
Abstract

The ever-growing developments in technology to capture different types of image data [e.g., hyperspectral imaging and light detection and ranging (LiDAR)-derived digital surface model (DSM)], along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this article, we propose a multisensor deep clustering (MDC) algorithm for the joint processing of multisource imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multisensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at [Online]. Available: https://github.com/Kasra2020/MDC.

Published
2021

55. Multimodal Fusion Transformer for Remote Sensing Image Classification

Author

Roy, Swalpa Kumar, Deria, Ankur, Hong, Danfeng, Rasti, Behnood, Plaza, Antonio, and Chanussot, Jocelyn

Abstract

Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared with convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViTs in hyperspectral image (HSI) classification tasks. To achieve satisfactory performance, close to that of CNNs, transformers need fewer parameters. ViTs and other similar transformers use an external classification (CLS) token, which is randomly initialized and often fails to generalize well, whereas other sources of multimodal datasets, such as light detection and ranging (LiDAR), offer the potential to improve these models by means of a CLS. In this article, we introduce a new multimodal fusion transformer (MFT) network, which comprises a multihead cross-patch attention (mCrossPA) for HSI land-cover classification. Our mCrossPA utilizes other sources of complementary information in addition to the HSI in the transformer encoder to achieve better generalization. The concept of tokenization is used to generate CLS and HSI patch tokens, helping to learn a distinctive representation in a reduced and hierarchical feature space. Extensive experiments are carried out on widely used benchmark datasets, i.e., the University of Houston (UH), Trento, University of Southern Mississippi Gulfpark (MUUFL), and Augsburg. We compare the results of the proposed MFT model with other state-of-the-art transformers, classical CNNs, and conventional classifiers models. The superior performance achieved by the proposed model is due to the use of mCrossPA. The source code will be made available publicly at https://github.com/AnkurDeria/MFT.

Published
2023
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57. Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples

Author

Rasti, Behnood, Ghamisi, Pedram, Seidel, Peter, Lorenz, Sandra, and Gloaguen, Richard

Subjects
data fusion, mineral exploration, feature extraction, spectral imaging, lcsh:Chemical technology, Article, hyperspectral, hyperspectral mixed sparse and Gaussian noise reduction (HyMiNoR), sparse and smooth low-rank analysis (SSLRA), multi-sensor data, orthogonal total variation component analysis (OTVCA), lcsh:TP1-1185, support vector machine (SVM), optical sensor, dimensionality reduction
Abstract

Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.

Published
2020
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73. Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art

Author

Ghamisi, Pedram, primary, Gloaguen, Richard, additional, Atkinson, Peter M., additional, Benediktsson, Jon Atli, additional, Rasti, Behnood, additional, Yokoya, Naoto, additional, Wang, Qunming, additional, Hofle, Bernhard, additional, Bruzzone, Lorenzo, additional, Bovolo, Francesca, additional, Chi, Mingmin, additional, and Anders, Katharina, additional

Published
2019
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