Segmenting greenhouse gases from hyperspectral images can provide detailed information regarding their spatial distribution, which is significant for the monitoring of greenhouse gases. However, accurate segmentation of greenhouse gases is a challenging task due to two main reasons: (1) Diversity: greenhouse gases vary in concentration, size, and texture; (2) Camouflage: the boundaries between greenhouse gases and the surrounding background are blurred. Existing methods primarily focus on designing new modules to address the above challenges, often neglecting the design of the upsampling method within the model, which is crucial for achieving accurate segmentation. In this work, we propose Gas-Aware Upsampling (GasUpper), a novel and efficient upsampling method tailored for greenhouse gas segmentation. Specifically, we first generate a coarse segmentation mask during the upsampling process. Based on the roughly segmented gas and background, we then extract the global features of the gas and combine them with the original features to obtain de-camouflaged feature map that include both the global characteristics of the gas and the local details of the image. This de-camouflaged feature map serves as the foundation for subsequent point sampling. Finally, we utilize the de-camouflaged feature map to generate upsampling coordinate offsets, enabling the model to adaptively adjust the sampling regions based on the content during the sampling process. We conduct comprehensive evaluations by replacing the upsampling method in various segmentation approaches with GasUpper on two hyperspectral datasets. The results indicate that GasUpper consistently and significantly enhances the performance across all segmentation models (0.08%–9.44% Intersection over Union (IoU), 0.47%–6.26% Accuracy), outperforming other upsampling methods.