Hyperspectral Remote Sensing Images Salient Object Detection: The First Benchmark Dataset and Baseline
Journal:
arXiv
Published Date:
Apr 3, 2025
Abstract
The objective of hyperspectral remote sensing image salient object detection
(HRSI-SOD) is to identify objects or regions that exhibit distinct spectrum
contrasts with the background. This area holds significant promise for
practical applications; however, progress has been limited by a notable
scarcity of dedicated datasets and methodologies. To bridge this gap and
stimulate further research, we introduce the first HRSI-SOD dataset, termed
HRSSD, which includes 704 hyperspectral images and 5327 pixel-level annotated
salient objects. The HRSSD dataset poses substantial challenges for salient
object detection algorithms due to large scale variation, diverse
foreground-background relations, and multi-salient objects. Additionally, we
propose an innovative and efficient baseline model for HRSI-SOD, termed the
Deep Spectral Saliency Network (DSSN). The core of DSSN is the Cross-level
Saliency Assessment Block, which performs pixel-wise attention and evaluates
the contributions of multi-scale similarity maps at each spatial location,
effectively reducing erroneous responses in cluttered regions and emphasizes
salient regions across scales. Additionally, the High-resolution Fusion Module
combines bottom-up fusion strategy and learned spatial upsampling to leverage
the strengths of multi-scale saliency maps, ensuring accurate localization of
small objects. Experiments on the HRSSD dataset robustly validate the
superiority of DSSN, underscoring the critical need for specialized datasets
and methodologies in this domain. Further evaluations on the HSOD-BIT and
HS-SOD datasets demonstrate the generalizability of the proposed method. The
dataset and source code are publicly available at
https://github.com/laprf/HRSSD.
