For several decades, the significance of geospatial object segmentation in remote-sensing images has been emphasized for both scientific and industrial purposes. Particularly, the importance of granularity in the boundaries of geospatial objects, called fine-grained segmentation, has been highlighted in achieving the precise identification and classification of objects. Deep learning-based segmentation methodologies, surpassing the limitations of the conventional vision-based analysis, have yielded accurate predictions by utilizing Convolutional Neural Networks (CNNs). However, CNNs classify images pixel-wise and produce outputs based on probability distributions derived from the SoftMax function. This approach precludes the reflection of morphological properties, such as shape and object density, during predictions in remote sensing imagery, leading to imprecise results. Additionally, due to the intrinsic attributes of probability-based segmentation, fine-grained segmentation may not be achieved, leading to coarse predictions in the boundaries of geospatial objects. To address this issue, this paper introduces a novel deep learning framework, the Density-based Guide Network (DG-Net), which incorporates the density of segmentation targets into pixel-wise classification through a test-time adaptation learning methodology. DG-Net first recognizes the density of segmentation targets in the input images, then fine-tunes the baseline network to reflect this density, generating precise segmentation outputs. The efficacy of DG-Net is demonstrated through various multi-target segmentation benchmarks in remote sensing imagery, with experimental results demonstrating superior performance compared to state-of-the-art models in object segmentation across numerous aerial image and satellite image datasets. IEEE
