CFMNet: A Lightweight Backbone With Cooperative Feature Modeling for Remote Sensing Vision Tasks

Towards Lightweight and Accurate Remote-Sensing Image Super-Resolution via Reparameterized Feature Enhancement Network

Remote sensing image super-resolution (RSISR) provides an effective means of improving spatial detail for Earth observation and satellite image interpretation. However, existing methods often rely on increasingly complex network designs with deeper hierarchies and expanded channel capacities to pursue higher performance, resulting in heavy models with high computational cost, which restricts their deployment on resource-constrained platforms. To address this challenge, we propose a novel reparameterized feature enhancement network (RepFEN) for lightweight and accurate RSISR tasks. Specifically, a multi-scale reparameterized module (MRepM) is designed to capture multi-scale spatial information and enhance texture representation. Furthermore, a partial-channel gated attention module (PCGAM) is introduced to selectively enhance discriminative features along the channel dimension, effectively improving fine-grained detail restoration. By integrating structural reparameterization and multi-scale lightweight modules, the proposed method achieves a better balance between reconstruction accuracy and inference efficiency. Extensive experiments on both remote sensing and natural image super-resolution benchmarks demonstrate that our method achieves superior performance compared to existing state-of-the-art methods, while maintaining minimal computational overhead, showing significant potential for real-world applications.

LCTCANet: a lightweight CNN-transformer context-aware network for remote sensing super-resolution

Remote sensing imagery is rich in textures and exhibits strong spatial autocorrelation, making both local and global feature extraction essential for super-resolution (SR). Although CNN-based SR methods effectively capture local patterns, their limited receptive field hinders long-range modeling, degrading reconstruction quality. Moreover, their high parameter counts and computational costs restrict deployment on resource-constrained platforms. To address these issues, we propose LCTCANet, a lightweight CNN-Transformer hybrid architecture for remote sensing SR. The network integrates a core feature extraction module composed of a global contextual local block (GCLB) and an edge-structure fusion block (ESFB). GCLB leverages multi-head self-attention to model global dependencies and enhance structural coherence. ESFB, by contrast, focuses on fine-grained local textures through edge-aware attention and structural refinement. Their fusion enables joint learning of global context and local detail, reinforcing high-frequency and structural information. Extensive experiments on NWPU-RESISC45, Draper, and UC Merced datasets demonstrate that LCTCANet achieves competitive SR performance while maintaining low computational cost and parameter efficiency.

QS-YOLOv8: a lightweight and efficient detector for small objects

MDCF-YOLO: a lightweight multi-scale context-aware and feature fusion network for small object detection in remote sensing images

MsRE: Toward Efficient Remote Sensing Segmentation via Vision Foundation Models

Vision foundation models (VFMs) pretrained on large-scale datasets have significantly improved performance in remote sensing semantic segmentation. However, existing methods typically rely on full fine-tuning, which requires updating all model parameters. Instead of updating the full parameter set, parameter-efficient fine-tuning (PEFT) achieves competitive performance by optimizing only a small subset of parameters. Despite its success, most existing PEFT methods are mainly designed for natural image tasks and fail to account for the unique multiscale characteristics of remote sensing images. To address these challenges, we propose multi-scale cognitive feature refinement (MsRE) tuning, a novel PEFT method tailored for remote sensing semantic segmentation. In particular, MsRE captures multiscale contextual information by applying cognitive operations with different cognitive fields to intermediate features of the backbone. It then introduces a set of learnable tokens to establish interactions with features at different scales, enabling precise feature refinement and progressive feature propagation across network layers. This mechanism enhances the model’s ability to understand complex remote sensing scenes and improves downstream segmentation performance. With significantly fewer trainable parameters, MsRE provides an efficient yet effective solution for adapting VFMs to remote sensing segmentation tasks. Extensive experiments demonstrate that MsRE achieves competitive segmentation performance with substantially fewer trainable backbone parameters, providing a favorable balance between accuracy and parameter efficiency. The project is available at http://woldier.top/MsRE

Mamba-Enhanced Lightweight Remote Sensing Object Detection

Remote sensing object detection (RSOD) aims to accurately identify and locate ground objects in remote sensing images, supporting applications, such as environmental monitoring, disaster assessment, uncrewed aerial vehicle perception, and satellite remote sensing. However, practical RSOD often requires real-time inference on large-scale high-resolution images under limited onboard or edge computing resources. Meanwhile, small objects, arbitrary orientations, complex backgrounds, and unstable imaging quality make it difficult for existing methods to balance lightweight deployment and high-precision detection. To address these challenges, we propose MELRNet, a Mamba-enhanced lightweight framework for remote sensing rotated object detection. Specifically, Mamba-style state space modeling is introduced into key semantic stages to capture long-range dependencies with linear complexity. A multi-scale receptive field aggregator is designed to enhance small-object and multiscale representation, while dynamic tanh normalization is adopted to improve feature stability with limited computational overhead. Extensive experiments on five benchmark datasets demonstrate that MELRNet achieves a favorable balance between lightweight design and high-precision rotated object detection.