The advancements in deep learning and computer vision technologies have had a profound impact on the field of air-borne remote sensing, making the analysis of aerial images more efficient. Compared to conventional images, the target boundaries in aerial images are clearer and more distinct, with more regular distributions and stronger spatial structure. However, current state-of-the-art segmentation methods mainly focus on utilizing complex feature extractors to capture stronger contextual relationships, placing more emphasis on single-pixel classification accuracy. This not only demands higher hardware requirements but also overlooks the issue of boundary alignment from a structural perspective. To address this challenge, we propose an innovative boundary-aware loss function, Lossd, designed to enhance the performance of semantic segmentation for aerial remote sensing images, particularly in terms of boundary precision and target segmentation consistency. We innovatively translate structural differences into a loss, unlike traditional methods that focus on single-pixel accuracy. Moreover, we propose an effective solution for the common over-segmentation and under-segmentation problems in semantic segmentation tasks. Extensive experimental validation has been conducted on three widely used large-scale datasets and three benchmark models. Experimental results show that our method significantly improves the semantic segmentation performance without modifying the original network. Specialty, our method achieves 55.8% mIoU (+1.6%) on LoveDA, 70.8% mIoU (+0.8%) on UAVid, and 94.1% mF1 (+0.7%) on Potsdam, reaching or partially surpassing the performance of mainstream approaches.

Matching remote sensing images taken by an unmanned aerial vehicle (UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset (University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method.