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Few-shot object detection (FSOD) in remote sensing images (RSIs) aims to achieve object location and classification with only a few training samples. Currently, mainstream transfer-learning methods employ a two-stage approach: pretraining on data-abundant base classes and fine-tuning on few-shot novel classes. However, existing approaches suffer notable degradation in both base and novel classes during fine-tuning, because of gradient conflict and class imbalance. To address this, we construct the balanced orthogonal subspace separation (BOSS) detector, a novel two-stage framework for FSOD. Specifically, to avoid contradictory gradients, BOSS distinctly isolates the training of base and novel classes at both structural and feature levels. For structural separation, a low-rank subspace adapter (LoSA) is introduced to ensure network optimization for novel classes without hampering base classes’ pretraining performance, effectively addressing over-fitting in few-shot scenarios. For feature disentanglement, an orthogonal subspace extractor (OSE) is presented, enhancing class separability by learning class-specific, orthogonal basis-spanned subspace. Finally, a balanced classifier (BC) is proposed to equalize the imbalanced loss, with its dual-component design mitigating bias toward predicting background or base classes. Comparative evaluations on diverse remote sensing datasets demonstrate BOSS’s superiority, outperforming state-of-the-art methods in mean average precision (mAP). These results underscore BOSS’s effectiveness in FSOD, particularly in challenging remote sensing contexts.

Recent approaches based on neural radiance field (NeRF) showcase remarkable results in the 3-D reconstruction of small-scale scenes by encoding volume density and color observations using implicit functions. However, when confronted with complex and diverse large-scale scenes, it invariably experiences issues such as blurry textures and missing details. In this work, we present a superpixel-based neural radiance field (named SuperNeRF), an additional loss for learning radiance fields that takes advantage of superpixels texture constraints. Building upon NeRF, we leverage superpixels to establish spatial consistency constraints, enabling the precise extraction of 3-D geometry and appearance for large-scale scenes. SuperNeRF is capable of guiding locally adjacent and similar pixels to form nearly consistent ray termination distributions, and it is compatible with the state-of-the-art NeRF-based methods. Comprehensive experiments conducted on representative aviation and aerospace datasets demonstrate that our SuperNeRF exhibits a significant superiority in accuracy over state-of-the-art methods. Code will be available at https://github.com/xczbecalm/supernerf.