Oriented object detection in remote sensing images rapidly evolves as a pivotal technique, driving transformative advancements across geospatial analytics, intelligent transportation systems, and urban infrastructure planning. However, the inherent characteristics of remote sensing objects, including complex background interference, multi-scale variations, and high-density distribution, pose critical challenges to balance detection accuracy and computational efficiency. This paper presents an anchor-free framework that eliminates the intrinsic constraints of anchor-based detectors, specifically the positive–negative sample imbalance and the computationally expensive non-maximum suppression (NMS) process. By effectively integrating adaptive kernel module with boundary refinement network, we achieved lightweight and efficient detection. Our method adaptively generates convolutional kernels tailored for multi-scale objects to extract discriminative features, while utilizing a boundary refinement network to precisely capture oriented bounding boxes. Experiments were carried out on the widely recognized HRSC2016 and DOTA datasets for the oriented bounding box (OBB) task. The proposed approach achieves 90.13% mAP (VOC07 metric) on HRSC2016 with 61.60 M parameters and 158.84 GFLOPS. For the DOTA benchmark, we attain 75.84% mAP with 45.96 M parameters and 131.39 GFLOPs. Our work highlights a lightweight yet powerful architecture that effectively balances accuracy and efficiency, making it particularly suitable for resource-constrained edge platforms.

Accurate hyperspectral image classification is fundamental to geospatial applications but is often constrained by annotation scarcity. To achieve high classification performance under small-sample conditions, we propose the Memory-Guided Adaptive Spectral–Spatial Perception model, which incorporates a three-level globalization strategy. At the single-sample level, an adaptive perception Transformer combines deformable and dilated convolutions with a Transformer encoder to capture global context from individual samples. At the intra-batch level, we introduce a metric learning strategy that explicitly captures structural dependencies and feature relationships among samples within each mini-batch, enabling comprehensive feature aggregation in a localized context. At the cross-batch level, a memory-guided strategy constructs a dynamic memory bank to store and retrieve features from same-class samples across training iterations, bridging past and present distributions to enhance generalization. Using only 1% of the SaliLMSS, Pavia University and Kennedy Space Center datasets and 0.5% of the WHU-LongKou dataset as training samples, our method achieves outstanding overall accuracy of 96.15%, 97.81%, 89.22% and 99.32%, respectively, outperforming existing methods.