Hyperspectral remote sensing images (HSIs) possess rich spectral resolution and spatial structural information, offering significant advantages in object detection tasks. Although existing approaches such as DSSN have made progress in this field, there remain substantial challenges, such as limited suppression of spectral redundancy, inadequate multi-scale feature fusion, insufficient global context modeling, and loss functions that are not fully adapted to the characteristics of HSIs. In this paper, we propose an enhanced deep network architecture that systematically incorporates four key innovations: a Spectral Squeeze-and-Excitation module (SpectralSE), a Feature Pyramid Decoder (FPNDecoder), a deep Spectral-Spatial Transformer module (SpecSpaTransformer), and a joint BCE + Dice loss function. The proposed method is thoroughly evaluated on HRSSD, the only publicly available benchmark dataset specifically designed for hyperspectral salient object detection. Experimental results demonstrate that our model consistently outperforms existing approaches across multiple mainstream metrics, achieving superior detection accuracy, structural consistency, and model robustness, thereby establishing a new state-of-the-art (SOTA) in the field.