It is difficult to accurately identify the winter wheat acreage in the Jianghuai region of China, and the fusion of high-resolution images and medium-resolution image data can improve the image quality and facilitate the identification and acreage extraction of winter wheat. Therefore, the objective of this study is to improve the accuracy of China’s medium-spatial resolution image data (environment and disaster monitoring and forecasting satellite data, HJ-1/CCD) in extracting the large area of winter wheat planted. The fusion and object-oriented classification of the 30 m × 30 m HJ-1/CCD multispectral image and 2 m × 2 m GF-1 panchromatic image (GF-1/PMS) of winter wheat at the jointing stage in the study area were studied. The GF-1/PMS panchromatic images were resampled at 8 m, 16 m and 24 m to produce panchromatic images with four spatial resolutions, including 2 m. They were fused with HJ-1/CCD multispectral images by Gram Schmidt (GS). The quality of the fused images was evaluated to pick adequate scale images for the field pattern of winter wheat cultivation in the study area. The HJ-1/CCD multispectral image was resampled to obtain an image with the same scale as the suitable scale fused image. In the two images, the training samples SFI (samples of fused image) and SRI (samples of resampled image) containing spectral and texture information were selected. The fused image (FI) and resampled image (RI) were used for winter wheat acreage extraction using an object-oriented classification method. The results indicated that the fusion effect of 16 m × 16 m fused image was better than 2 m × 2 m, 8 m × 8 m and 24 m × 24 m fused images, with mean, standard deviation, average gradient and correlation coefficient values of 161.15, 83.01, 4.55 and 0.97, respectively. After object-oriented classification, the overall accuracy of SFI for the classification of resampled image RI_16m was 92.22%, and the Kappa coefficient was 0.90. The overall accuracy of SFI for the classification of fused image FI_16m was 94.44%, and the Kappa coefficient was 0.93. The overall accuracy of SRI for the classification of resampled image RI_16m was 84.44%, and the Kappa coefficient was 0.80. The classification effect of SFI for the fused image FI_16m was the best, indicating that the object-oriented classification method combined with the fused image and the extraction samples of the fused image (SFI) could extract the winter wheat planting area with precision. In addition, the object-oriented classification method combining resampled images and the extraction samples of fused images (SFI) could extract the winter wheat planting area more effectively. These results indicated that the combination of medium spatial resolution HJ-1/CCD images and high spatial resolution GF-1 satellite images could effectively extract the planting area information of winter wheat in large regions.

Fine-grained monitoring of aquatic vegetation in crab ponds is essential for regulating water quality, sustaining ecological balance, and optimizing Chinese mitten crab (Eriocheir sinensis) aquaculture. However, owing to the complex water environment, fragmented vegetation morphology, and the absence of dedicated annotated datasets, traditional remote sensing techniques struggle to achieve highly accurate semantic segmentation and classification. In this study, we construct the first unmanned aerial vehicle (UAV) multispectral dataset for crab pond aquatic vegetation, encompassing four species, Alternanthera philoxeroides, Vallisneria natans, Hydrilla verticillata, and Elodea nuttallii, with pixel-level annotations verified by field surveys across typical aquaculture sites in Jiangsu Province, China. Furthermore, we introduce the Multi-scale Frequency–Spatial Collaborative Network (MFSCNet), built upon a MedNeXt backbone and augmented with distributed modules, including Channel Reduction Attention, Spatial Frequency Selection, a spatial–frequency fusion module, and Mobile Graph Convolution that operate cooperatively across the encoder, skip connections, decoder, and output head. This design suppresses complex water-background interference, enhances vegetation texture representation, and preserves the spatial continuity of vegetation patches. Experimental results demonstrate that, with a lightweight parameter size of merely 19.38 M, MFSCNet achieves a remarkable mean Intersection over Union (mIoU) of 0.9044, outperforming various mainstream convolutional neural network (CNN) and Transformer-based architectures. This study not only provides a high-precision remote sensing technical framework for the accurate multi-class identification and quantitative assessment of aquatic vegetation in crab ponds but also establishes reliable data support for refined aquaculture management and aquatic ecological conservation.