With technological advancements, combining information from various tasks has become increasingly important. However, most feature learning approaches still focus on single-task learning. To address this, we propose a multitask learningbased model that simultaneously performs segmentation and saliency estimation. Our model is evaluated on two hyperspectral datasets: HS-SOD for computer vision and Pavia University (PU) for remote sensing, which demonstrate strong generalization capabilities. By utilizing the additional spectral dimension in hyperspectral data, the model improves its ability to distinguish between materials and objects, leading to higher accuracy. The architecture features a shared encoder-decoder structure for efficiency, with an attention block enhancing segmentation by capturing key spectral-spatial features and a dense ASPP block improving salient object detection through multi-scale context. Extensive testing shows our model outperforms single-task approaches and state-of-the-art methods, proving its effectiveness and efficiency.