HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing
Hyperspectral image (HSI) dehazing is a challenging task due to the complex imaging conditions. Existing deep learning-based dehazing methods neither fully consider the physical characteristics of HSIs, nor take advantage of high-level semantic information to improve the dehazing performance. To rem...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10820032/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Hyperspectral image (HSI) dehazing is a challenging task due to the complex imaging conditions. Existing deep learning-based dehazing methods neither fully consider the physical characteristics of HSIs, nor take advantage of high-level semantic information to improve the dehazing performance. To remedy these, in this article we propose a Haze Density and Semantic Awareness Network (HDSA-Net) for HSI dehazing. Our dual-awareness network not only provides low-level physical information guidance but also high-level semantic guidance for haze removal. Specifically, we estimate the haze density by considering both internal spectral characteristics and external dehazing effects. Based on this, we build a Haze Density Awareness (HDA) block, which enables the network to perceive and focus on difficult dehazing regions with high density. Moreover, we design a Semantic information Extraction Block (SEB) based on the pretrained Segment Anything Model (SAM), followed by several Semantic information Perception Blocks (SPBs), to provide semantic guidance for HSI dehazing. In particular, SEB adapts SAM for the special HSI data and SPBs enable the network to progressively recover semantic information via channel-level coarse guidance and pixel-level fine guidance. The experimental results on simulated and real datasets show the superiority of HDSA-Net over state-of-the-art methods. |
|---|---|
| ISSN: | 1939-1404 2151-1535 |