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Self-supervised Remote Sensing Images Change Detection at Pixel-level
Yuxing Chen, Lorenzo Bruzzone
Deep learning techniques have achieved great success in remote sensing image change detection. Most of them are supervised techniques, which usually require large amounts of traini…
Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification
Lichao Mou, Sudipan Saha, Yuansheng Hua +3
Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model train…
Self-supervised Change Detection in Multi-view Remote Sensing Images
Yuxing Chen, Lorenzo Bruzzone
The vast amount of unlabeled multi-temporal and multi-sensor remote sensing data acquired by the many Earth Observation satellites present a challenge for change detection. Recentl…
Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images
Yuxing Chen, Lorenzo Bruzzone
The effective combination of the complementary information provided by the huge amount of unlabeled multi-sensor data (e.g., Synthetic Aperture Radar (SAR) and optical images) is a…
Remote Sensing Image Scene Classification with Deep Neural Networks in JPEG 2000 Compressed Domain
Akshara Preethy Byju, Gencer Sumbul, Begüm Demir +1
To reduce the storage requirements, remote sensing (RS) images are usually stored in compressed format. Existing scene classification approaches using deep neural networks (DNNs) r…
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification
Yao Qin, Lorenzo Bruzzone, Biao Li
This paper presents a tensor alignment (TA) based domain adaptation method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented in…