Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection

Jijun Xiang; Tao Wang; Jiayi Wang; Pengxiang Wang; Cheng Chen; Nian Wang

cs.CVApr 13, 2026

Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection

Authors: Jijun Xiang, Tao Wang, Jiayi Wang, Pengxiang Wang, Cheng Chen, Nian Wang

Organizations: Rocket Force University of Engineering, Xi’an 710025, China

Abstract

While Hyperspectral Anomaly Detection (HAD) excels at identifying sparse targets in complex scenes, existing models remain trapped in a scalar "reconstruction-as-endpoint" paradigm. This reliance on ambiguous scalar residuals consistently triggers sub-pixel anomaly vanishing during spatial downsampling, alongside severe confirmation bias when unpurified anomalies corrupt training weights. In this paper, we propose Reconstruction-to-Vector Diffusion (R2VD), which fundamentally redefines reconstruction as a manifold purification origin to establish a novel residual-guided generative dynamics paradigm. Our framework introduces a four-stage pipeline: (1) a Physical Prior Extraction (PPE) stage that mitigates early confirmation bias via dual-stream statistical guidance; (2) a Guided Manifold Purification (GMP) stage utilizing an OmniContext Autoencoder (OCA) to extract purified residual maps while preserving fragile sub-pixel topologies; (3) a Residual Score Modeling (RSM) stage where a Diffusion Transformer (DiT), guarded by a Physical Spectral Firewall (PSF), effectively isolates cross-spectral leakage; and (4) a Vector Dynamics Inference (VDI) stage that robustly decouples targets from backgrounds by evaluating high-dimensional vector interference patterns instead of conventional scalar errors. Comprehensive evaluations on eight datasets confirm that R2VD establishes a new state-of-the-art, delivering exceptional target detectability and background suppression. The code is available at https://github.com/Bondojijun/R2VD.

Figures & tables

Fig. 1: Comparison of HAD residual evaluation approaches. (a) Traditional scalar reconstruction using the $L_{2}$ norm of the residual as the detection metric. (b) Proposed Reconstruction-to-Vector pipeline, which repurposes reconstruction for manifold purification to enable subsequent high-dimensional vector dynamics inference.

Fig. 2: Visual and spectral analysis on the HAD100-40 dataset. Top: Detection results comparing traditional methods and R2VD. Bottom: Spectral curves of selected pixels.

Fig. 3: Architecture of the proposed R2VD framework. Physical Prior Extraction: Generates a lenient initial weight map ( $\mathbf{W}_{coa}$ ) from dual-stream detectors (RX and LSUN) via PRA. Guided Manifold Purification: Decouples anomalies using the OCA with dynamic weight iterations, outputting a purified residual map and a weight map ( $\mathbf{W}$ ). Residual Score Modeling: A DiT learns the background score field under the PSF constraint. Vector Dynamics Inference: Injects $K$ perturbations into the frozen DiT field, leveraging the vector cancellation of background gradients and the deterministic vector addition of anomalous gradients to generate the final Anomaly Map.

Fig. 4: Illustration of weight function dynamics. The blue dashed curve represents the lenient weight

\mathbf{W}_{coa}

in the PPE stage to mitigate early confirmation bias. The red solid curve denotes the strict truncation weight

\mathbf{W}

in the GMP stage, featuring an absolute gradient isolation at

t=1.00

. Background colors partition the score space into consensus background (green), divergence region (white), and consensus anomaly (red).

Fig. 5: Architecture of the OCA. (a) The macro-architecture preserves the $H\times W$ spatial dimensions to prevent sub-pixel anomaly vanishing. (b) The OCB extracts multi-scale and directional context features via four parallel branches. (c) The RSB deepens spectral non-linearity using $1\times 1$ convolutions without spatial blurring.

Fig. 6: Architecture of the RSM module. (a) The DiT block incorporating AdaLN-Zero modulation for timestep conditioning. (b) The mechanism of the PSF.

Dataset Name	Spatial Size	Bands	Anomaly Pixels	Ratio (%)
Urban-2 [ 48 ]	100 $\times$ 100	207	155	1.55
Aviris-2 [ 49 ]	128 $\times$ 128	189	120	0.73
Cri [ 51 ]	400 $\times$ 400	46	860	0.54
HAD100-40 [ 6 ]	64 $\times$ 64	276	67	1.64
HAD100-95 [ 6 ]	64 $\times$ 64	276	85	2.08
Hyperion [ 50 ]	100 $\times$ 100	145	32	0.32

TABLE I: Detailed Information of the Eight Hyperspectral Datasets.

Fig. 7: 2D ROC curves of the proposed R2VD and other state-of-the-art anomaly detection methods across eight hyperspectral datasets. (a) Urban-2. (b) Aviris-2. (c) Cri. (d) HAD100-40. (e) HAD100-95. (f) Hyperion. (g) Segundo. (h) UHAD-I. In most evaluated datasets, R2VD maintains a stable position in the upper-left region.

Dataset	Metric	RX [ 19 ]	LSUN [ 20 ]	Auto-AD [ 10 ]	GT-HAD [ 11 ]	GAED [ 7 ]	NL2Net [ 55 ]	PDBSNet [ 53 ]	PUNNet [ 54 ]	BSDM [ 16 ]	ScoreAD [ 47 ]	Auto-AD +VD	GT-HAD +VD	R2VD
Urban-2 [ 48 ]	$AUC_{(D,F)}\uparrow$	0.9946	0.9721	0.9970	0.9953	0.9958	0.9969	0.9960	0.9951	0.9964	0.9867	0.9950	0.9974	0.9975
	$AUC_{(D,\tau)}\uparrow$	0.1178	0.0653	0.2666	0.2183	0.1098	0.2718	0.1842	0.2317	0.1649	0.9577	0.3540	0.2989	0.7891
	$AUC_{(F,\tau)}\downarrow$	0.0135	0.0020	0.0031	0.0081	0.0045	0.0007	0.0011	0.0007	0.0133	0.7395	0.0244	0.0182	0.0471
	$AUC_{OD}\uparrow$	1.0989	1.0354	1.2605	1.2055	1.1011	1.2680	1.1790	1.2262	1.1480	1.2049	1.3247	1.2782	1.7395
	$AUC_{SNPR}\uparrow$	8.7151	32.4981	86.0995	27.0674	24.3536	372.9856	163.9466	352.3328	12.3745	1.2951	14.5160	16.4608	16.7709
Aviris-2 [ 49 ]	$AUC_{(D,F)}\uparrow$	0.9181	0.9150	0.8950	0.8968	0.9034	0.9389	0.9453	0.9365	0.8967	0.6207	0.9241	0.9228	0.9878

TABLE II: Quantitative results on eight datasets. Red , dark green , and blue denote the first, second, and third results, respectively, ranked across all methods.

Fig. 8: Visual comparison of anomaly detection maps on eight hyperspectral datasets. (a) Urban-2. (b) Aviris-2. (c) Cri. (d) HAD100-40. (e) HAD100-95. (f) Hyperion. (g) Segundo. (h) UHAD-I. R2VD distinctly highlights anomalies while facilitating the preservation of sub-pixel targets and topological structures.

Fig. 9: Statistical separability of anomaly (red) and background (blue) pixels across eight datasets, evaluated by normalized detection statistics. Boxes and whiskers indicate the interquartile range and statistical extent across thirteen methods, including the proposed R2VD, with the methods displayed in two subpanels for each dataset. (a1)–(a2) Urban-2. (b1)–(b2) Aviris-2. (c1)–(c2) Cri. (d1)–(d2) HAD100-40. (e1)–(e2) HAD100-95. (f1)–(f2) Hyperion. (g1)–(g2) Segundo. (h1)–(h2) UHAD-U-I. Results show R2VD yields competitive anomaly-background separation.

Metric	Aviris-2	HAD100-95	Segundo
$AUC_{(D,F)}$	0.9865 $\pm$ 0.0012	0.9944 $\pm$ 0.0010	0.9932 $\pm$ 0.0008
$AUC_{(D,\tau)}$	0.5507 $\pm$ 0.0125	0.5015 $\pm$ 0.0173	0.4618 $\pm$ 0.0130
$AUC_{(F,\tau)}$	0.0500 $\pm$ 0.0035	0.0446 $\pm$ 0.0019	0.0008 $\pm$ 0.0002
$AUC_{OD}$	1.4872 $\pm$ 0.0142	1.4513 $\pm$ 0.0163	1.4543 $\pm$ 0.0133
$AUC_{SNPR}$	11.0700 $\pm$ 0.8100	11.2500 $\pm$ 0.4800	638.6604 $\pm$ 180.3332

TABLE III: Statistical Reliability Analysis of the R2VD Over Five Random Seeds.

Dataset	$K$	$AUC_{(D,F)}\uparrow$	BSV $\downarrow$	$\mathrm{FAR}_{95}\downarrow$
Aviris-2	5	0.9017 $\pm$ 0.0108	7.72 ${\times}10^{-4}$	0.5818 $\pm$ 0.0851
Aviris-2	50	0.9865 $\pm$ 0.0012	1.15 ${\times}10^{-4}$	0.0844 $\pm$ 0.0134
HAD100-95	5	0.9619 $\pm$ 0.0081	7.55 ${\times}10^{-4}$	0.2587 $\pm$ 0.0832
HAD100-95	50	0.9944 $\pm$ 0.0010	9.63 ${\times}10^{-5}$	0.0126 $\pm$ 0.0022
Segundo	5	0.9440 $\pm$ 0.0024	7.55 ${\times}10^{-4}$	0.4482 $\pm$ 0.0401
Segundo	50	0.9932 $\pm$ 0.0008	1.22 ${\times}10^{-4}$	0.0459 $\pm$ 0.0106

TABLE IV: Perturbation stability comparison of VDI with

K=5

and

K=50

over five noise seeds.

Fig. 10: Structural illustration of the progressively constructed ablation variants.

Fig. 11: Visual evolution of anomaly detection maps across progressive ablation variants. Results confirm that each component mitigates background interference and enhances target-background contrast.

Dataset	Model	$AUC_{(D,F)}\uparrow$	$AUC_{SNPR}\uparrow$	SS $\uparrow$
Aviris-2	M0	0.7108	1.226	0.1035
	M1	0.7851	2.786	0.1311
	M2	0.8254	3.196	0.1712
	M3	0.8930	5.626	0.2671
	R2VD	0.9878	10.196	0.5127
HAD100-95	M0	0.7233	1.268	0.1096

TABLE V: Quantitative ablation study of the core components in the proposed R2VD framework.

Fig. 12: Hyperparameter sensitivity analysis across four HSI datasets. (a) Micro-perturbation count $K$ . (b) PSF penalty constant $\lambda$ . (c) Global anomaly prior ratio $\eta$ . (d) Inference time step $t_{\text{inf}}$ . (e) Window size $win$ . (f) Corresponding running time under different window sizes. The shared legend is in (a). Stable trajectories demonstrate the framework’s robustness and adaptive error correction.

Method	Params (M)	Total Running Time (s)
RX [ 19 ]	–	0.18
LSUN [ 20 ]	–	7.04
Auto-AD [ 10 ]	3.25	4.60
GT-HAD [ 11 ]	0.26	6.80
GAED [ 7 ]	0.05	36.13
NL2Net [ 55 ]	0.77	138.69

TABLE VI: Complexity and end-to-end running time comparison on the Urban-2 dataset.

Fig. 13: Visual analysis of the PSF mechanism across varying anomaly scales. By incorporating the physical penalty ( $\lambda=5$ ), the PSF contributes to the formation of highly localized spectral clusters.

Fig. 14: Three-dimensional anomaly score response maps on the Segundo dataset. By avoiding spatial smoothing, R2VD mitigates the attenuation of miniature anomalies.

Fig. 15: Visual comparison of detection maps across different stages. Red boxes and dashed lines indicate magnified regions. While the intermediate GMP stage effectively suppresses the prominent false alarms inherited from coarse detectors, it inevitably smooths out fragile target profiles. The complete R2VD framework successfully rectifies this structural attenuation, recovering sharp and high-contrast anomaly topologies.

Fig. 16: Visual comparison of weight map dynamics on the HAD100-95 dataset. Row (a) illustrates the purification process guided by the initial physical prior ( $\mathbf{W}_{coa}$ ). Row (b) shows the unguided autoencoder baseline against the Ground Truth. The synergy between PPE and GMP effectively self-corrects cognitive biases, generating a more robust gradient-gating mask for subsequent score field modeling.

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Sep 8, 2026J/K move · Enter open · S save

Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping

Quan Yu, Yu-Hong Dai, Xiongjun Zhang

School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China, and also with the Key Laboratory of Nonlinear Analysis and Applications (Ministry of Education), Central China Normal University · State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China, and also with the School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

Low-Rank Matrix Decomposition Tensor Decomposition Hyperspectral Imaging Anomaly Detection

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Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computational cost and limited flexibility in characterizing spatially structured anomalies. To address these issues, this paper proposes a hyperspectral anomaly detection method based on group sparse low-rank tensor factorization with automatic anomaly grouping (GSAA). Specifically, the low tubal rank background is characterized by imposing group sparsity on tensor factors, which provides an efficient alternative to direct tensor rank regularization. For anomaly modeling, a latent grouping map is introduced to build an automatic anomaly grouping penalty, allowing anomaly groups to be adaptively inferred from the data rather than predefined at the pixel level. To further exploit complementary spectral and spatial information, GSAA is applied in both domains, and the resulting detection maps are fused to form a spectral--spatial version of GSAA, termed GSAA-SS. An efficient linearized alternating direction method of multipliers algorithm with convergence guarantee is developed to solve the resulting model. Experimental results on five real hyperspectral datasets demonstrate that the proposed method achieves superior detection performance and competitive computational efficiency compared with several state-of-the-art methods.

Abstract

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