Highlights What are the main findings? Spectral reconstruction can support small-target detection in multispectral imagery when it is adapted to the target of interest. Spectral reconstruction models trained without target-aware adaptation may fail to preserve discriminative target features and lead to reduced detection performance. What are the implications of the main findings? The usefulness of spectral reconstruction for downstream applications should be evaluated using task-specific criteria rather than reconstruction accuracy alone. Task-aware spectral reconstruction enables more reliable use of multispectral sensors for small-target detection, as shown in UAV-based scenarios.Highlights What are the main findings? Spectral reconstruction can support small-target detection in multispectral imagery when it is adapted to the target of interest. Spectral reconstruction models trained without target-aware adaptation may fail to preserve discriminative target features and lead to reduced detection performance. What are the implications of the main findings? The usefulness of spectral reconstruction for downstream applications should be evaluated using task-specific criteria rather than reconstruction accuracy alone. Task-aware spectral reconstruction enables more reliable use of multispectral sensors for small-target detection, as shown in UAV-based scenarios.Abstract Hyperspectral sensors provide high spectral resolution, enabling accurate material discrimination and effective target detection. However, their practical use is constrained by limited spatial resolution and high acquisition costs. This paper proposes a novel framework to enhance small-target detection in multispectral imagery by leveraging deep learning-based spectral reconstruction to generate high-resolution hyperspectral representations from multispectral inputs. Two state-of-the-art reconstruction networks, MST++ and MIRNet, are trained using paired multispectral-hyperspectral samples derived from AVIRIS-NG data through proper spectral response functions. To improve discriminative capability for the target of interest, a rapid, target-specific fine-tuning stage is introduced, allowing the models to adapt to spectral signatures that are poorly represented or absent in the original training data. Target detection is performed using a spectral signature-based detector applied to the reconstructed hyperspectral data. The proposed framework is evaluated in a real-world scenario involving known field-deployed targets and hyperspectral imagery acquired from an unmanned aerial vehicle. Experimental results demonstrate that the proposed approach significantly outperforms baseline detection applied directly to multispectral data. These findings underscore the effectiveness of spectral reconstruction for downstream tasks such as target detection, particularly in scenarios where hyperspectral data are expensive or unavailable.
Improved Multispectral Target Detection Using Target-Specific Spectral Reconstruction
Acito N.
Primo
Methodology
;
2026-01-01
Abstract
Highlights What are the main findings? Spectral reconstruction can support small-target detection in multispectral imagery when it is adapted to the target of interest. Spectral reconstruction models trained without target-aware adaptation may fail to preserve discriminative target features and lead to reduced detection performance. What are the implications of the main findings? The usefulness of spectral reconstruction for downstream applications should be evaluated using task-specific criteria rather than reconstruction accuracy alone. Task-aware spectral reconstruction enables more reliable use of multispectral sensors for small-target detection, as shown in UAV-based scenarios.Highlights What are the main findings? Spectral reconstruction can support small-target detection in multispectral imagery when it is adapted to the target of interest. Spectral reconstruction models trained without target-aware adaptation may fail to preserve discriminative target features and lead to reduced detection performance. What are the implications of the main findings? The usefulness of spectral reconstruction for downstream applications should be evaluated using task-specific criteria rather than reconstruction accuracy alone. Task-aware spectral reconstruction enables more reliable use of multispectral sensors for small-target detection, as shown in UAV-based scenarios.Abstract Hyperspectral sensors provide high spectral resolution, enabling accurate material discrimination and effective target detection. However, their practical use is constrained by limited spatial resolution and high acquisition costs. This paper proposes a novel framework to enhance small-target detection in multispectral imagery by leveraging deep learning-based spectral reconstruction to generate high-resolution hyperspectral representations from multispectral inputs. Two state-of-the-art reconstruction networks, MST++ and MIRNet, are trained using paired multispectral-hyperspectral samples derived from AVIRIS-NG data through proper spectral response functions. To improve discriminative capability for the target of interest, a rapid, target-specific fine-tuning stage is introduced, allowing the models to adapt to spectral signatures that are poorly represented or absent in the original training data. Target detection is performed using a spectral signature-based detector applied to the reconstructed hyperspectral data. The proposed framework is evaluated in a real-world scenario involving known field-deployed targets and hyperspectral imagery acquired from an unmanned aerial vehicle. Experimental results demonstrate that the proposed approach significantly outperforms baseline detection applied directly to multispectral data. These findings underscore the effectiveness of spectral reconstruction for downstream tasks such as target detection, particularly in scenarios where hyperspectral data are expensive or unavailable.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
