# DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection

arXiv: https://arxiv.org/abs/2404.00694 · HTML: https://mlsanity.com/paper/2404.00694/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 67 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Salient Object Detection via Integrity Learning](https://mlsanity.com/paper/2101.07663/) (2021)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Salient Object Detection: A Discriminative Regional Feature Integration Approach](https://mlsanity.com/paper/1410.5926/) (2014)
- [EDN: Salient Object Detection via Extremely-Downsampled Network](https://mlsanity.com/paper/2012.13093/) (2020)
- [Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/1906.08462/) (2019)
- [FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification](https://mlsanity.com/paper/2011.05670/) (2020)
- [Weakly-Supervised Salient Object Detection Using Point Supervision](https://mlsanity.com/paper/2203.11652/) (2022)
- [Hyperspectral Image Dataset for Benchmarking on Salient Object Detection](https://mlsanity.com/paper/1806.11314/) (2018)
- [Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2112.01932/) (2021)
- [Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment](https://mlsanity.com/paper/2301.02778/) (2023)
- [Distortion-adaptive Salient Object Detection in 360^circ Omnidirectional Images](https://mlsanity.com/paper/1909.04913/) (2019)
- [Weakly Supervised Video Salient Object Detection via Point Supervision](https://mlsanity.com/paper/2207.07269/) (2022)
- [Salient object detection on hyperspectral images using features learned from unsupervised segmentation task](https://mlsanity.com/paper/1902.10993/) (2019)
- [Factorization Vision Transformer: Modeling Long Range Dependency with Local Window Cost](https://mlsanity.com/paper/2312.08614/) (2023)

## Built on by (most cited first, 11 of 11)

- [MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking](https://mlsanity.com/paper/2503.17699/) (2025)
- [Content-driven Magnitude-Derivative Spectrum Complementary Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2407.18593/) (2024)
- [Spectrum-oriented Point-supervised Saliency Detector for Hyperspectral Images](https://mlsanity.com/paper/2412.18112/) (2024)
- [Mixed-granularity Implicit Representation for Continuous Hyperspectral Compressive Reconstruction](https://mlsanity.com/paper/2503.12783/) (2025)
- [Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2502.06427/) (2025)
- [Hyperspectral Remote Sensing Images Salient Object Detection: The First Benchmark Dataset and Baseline](https://mlsanity.com/paper/2504.02416/) (2025)
- [Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer](https://mlsanity.com/paper/2506.21880/) (2025)
- [DualGazeNet: A Biologically Inspired Dual-Gaze Query Network for Salient Object Detection](https://mlsanity.com/paper/2511.18865/) (2025)
- [Exploring Spatiotemporal Feature Propagation for Video-Level Compressive Spectral Reconstruction: Dataset, Model and Benchmark](https://mlsanity.com/paper/2603.00611/) (2026)
- [Spectral-Spatial Synergistic Guided Network for Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2607.21032/) (2026)
- [Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum](https://mlsanity.com/paper/2608.30355/) (2026)

## Abstract (arXiv)

Hyperspectral salient object detection (HSOD) has exhibited remarkable promise across various applications, particularly in intricate scenarios where conventional RGB-based approaches fall short. Despite the considerable progress in HSOD method advancements, two critical challenges require immediate attention. Firstly, existing hyperspectral data dimension reduction techniques incur a loss of spectral information, which adversely affects detection accuracy. Secondly, previous methods insufficiently harness the inherent distinctive attributes of hyperspectral images (HSIs) during the feature extraction process. To address these challenges, we propose a novel approach termed the Distilled Mixed Spectral-Spatial Network (DMSSN), comprising a Distilled Spectral Encoding process and a Mixed Spectral-Spatial Transformer (MSST) feature extraction network. The encoding process utilizes knowledge distillation to construct a lightweight autoencoder for dimension reduction, striking a balance between robust encoding capabilities and low computational costs. The MSST extracts spectral-spatial features through multiple attention head groups, collaboratively enhancing its resistance to intricate scenarios. Moreover, we have created a large-scale HSOD dataset, HSOD-BIT, to tackle the issue of data scarcity in this field and meet the fundamental data requirements of deep network training. Extensive experiments demonstrate that our proposed DMSSN achieves state-of-the-art performance on multiple datasets. We will soon make the code and dataset publicly available on https://github.com/anonymous0519/HSOD-BIT.