Although deep neural networks have achieved great success in many important remote sensing tasks, generating realistic remote sensing images from text descriptions is still very difficult. To address this challenge, we develop novel algorithms for high-resolution remote sensing image synthesis from text descriptions.
Y. Xu, W. Yu, P. Ghamisi, M. Kopp, S. Hochreiter, "Txt2Img-MHN: Remote sensing image generation from text using modern hopfield networks," arXiv preprint arXiv:2208.04441, 2022.
As an interdisciplinary field at the intersection of CV and NLP, image captioning has gained significant attention in recent years, with many researchers applying advanced artificial intelligence techniques to improve the state of the art. To address the challenge of generating accurate captions, we conduct interesting research for high-resolution remote sensing images.
S. Chang and P. Ghamisi, "Changes to Captions: An Attentive Network for Remote Sensing Change Captioning," arXiv reprint arXiv:2304.01091, 2023.
Adversarial Attacks and Defenses
While deep learning algorithms have achieved state-of-the-art performance in many remote sensing interpretation tasks, their vulnerability to adversarial examples should not be neglected. To tackle this challenge, we develop novel adversarial attack and defense methods and collect benchmark datasets for remote sensing images.
Y. Xu, P. Ghamisi, "Universal adversarial examples in remote sensing: Methodology and benchmark," IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1−15, 2022.
Natural Hazards Monitoring
Natural hazards are naturally occurring phenomena of geological, hydrological, or meteorological origin that might harm humans or the environment. To monitor and analyze natural hazards like landslides, we develop novel algorithms and collect benchmark datasets with multi-source remote sensing data.
O. Ghorbanzadeh, Y. Xu, P. Ghamisi, M. Kopp, D. Kreil, "Landslide4sense: Reference benchmark data and deep learning models for landslide detection," arXiv preprint arXiv:2206.00515, 2022.
AI for Social Good
New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. To better monitor and achieve the sustainable development goals, we develop novel AI approaches for social good.
C. Persello, J. Dirk Wegner, R. Hänsch, D. Tuia, P. Ghamisi, M. Koeva, G. Camps-valls, "Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities," IEEE Geosci. Remote Sens. Mag., 2022.
The quality of data acquired by remotely sensed imaging sensors (active and passive) is often degraded by a variety of noise types and artifacts. To tackle this challenge, we develop novel image restoration methods for remote sensing data.
B. Rasti, Y. Chang, E. Dalsasso, L. Denis, P. Ghamisi, "Image restoration for remote sensing: Overview and toolbox," IEEE Geosci. Remote Sens. Mag., 2021.
Due to the limited spatial resolution and scattering of the light, a pixel spectrum is generally a complex mixture of the pure spectra of its constituent materials, i.e., the endmember spectra. To accurately estimate the fractional abundances of those endmembers, we develop advanced deep learning-based unmixing methods.
B. Rasti, B. Koirala, P. Scheunders, P. Ghamisi, "Undip: Hyperspectral unmixing using deep image prior," IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1−15, 2022.
Traditional clustering models essentially work in the Euclidean domain, leading to insufficient handling of graph-structured data. To tackle this challenge, we develop novel graph neural network-based approaches for high-dimensional remote sensing data.
Y. Cai, Z. Zhang, Z. Cai, X. Liu, Y. Ding, P. Ghamisi, "Fully linear graph convolutional networks for semi-supervised learning and clustering," arXiv preprint arXiv:2111.07942, 2021.
Learning with Sparse Annotations
Most of the existing interpretation approaches, especially deep learning-based ones, require a large number of high-quality labeled samples, which are expensive and time-consuming to be collected in practice. To tackle this challenge, we develop novel algorithms that can achieve satisfactory performance with sparse annotations.
Y. Xu, P. Ghamisi, "Consistency-regularized region-growing network for semantic segmentation of urban scenes with point-level annotations," IEEE Trans. Image Process., vol. 31, pp. 5038–5051, 2022.
J. Yue et al., "Optical Remote Sensing Image Understanding With Weak Supervision: Concepts, methods, and perspectives," IEEE Geoscience and Remote Sensing Magazine, vol. 10, no. 2, pp. 250-269, June 2022,
Anomaly (Change) Detection
Traditional representation-based detectors suffer from high computation burden and can hardly explore the relationship among different instances. To tackle this challenge, we develop novel collaborative representation methods for high-dimensional remote sensing data without supervision.
S. Chang, P. Ghamisi, "Nonnegative-constrained joint collaborative representation with union dictionary for hyperspectral anomaly detection," IEEE Trans. Geosci. Remote. Sens., vol. 60, pp. 1-13, 2022.
S. Chang, M. Kopp, and P. Ghamisi, "Sketched Multiview Subspace Learning for Hyperspectral Anomalous Change Detection," IEEE Trans. Geosci. Remote. Sens., vol. 60, pp. 1-12, 2022.
Remote sensing data acquired from different sensors generally possess complementary information which is beneficial to the interpretation of ground objects. To make full use of this advantage, we develop novel data fusion approaches with deep learning for multi-source remote sensing data.
K. Rafiezadeh Shahi, P. Ghamisi, B. Rasti, P. Scheunders, R. Gloaguen, "Unsupervised data fusion with deeper perspective: A novel multisensor deep clustering algorithm," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 284−296, 2022.
P. Ghamisi et al., "Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art," IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 1, pp. 6-39, March 2019.