Object detection in remote sensing images (RSIs) requires the locating and classifying of
objects of interest, which is a hot topic in RSI analysis research. With the development of …

Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …

Few-shot object detection (FSOD) has received numerous attention due to the difficulty and
time-consuming of labeling objects. Recent researches achieve excellent performance in a …

Hyperspectral object tracking is aimed at tracking targets by using both spatial information
and abundant spectral information, overcoming the drawbacks of traditional RGB tracking in …

Remote sensing object detection (RSOD), one of the most fundamental and challenging
tasks in the remote sensing field, has received long-standing attention. In recent years, deep …

Abstract Knowledge distillation (KD) has been one of the most potential methods to
implement a lightweight detector, which plays a significant role in satellite in-orbit processing …

With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …

Despite the remarkable progress of general object detection, the lack of labeled aerial
images limits the robustness and generalization of the detector. Teacher–student learning is …

Accurate and timely assessment of post-disaster building damage is of great significance for
national development and social security concerns. However, due to the high timeliness …

Semantic segmentation necessitates approaches that learn high-level characteristics while
dealing with enormous quantities of data. Convolutional neural networks (CNNs) can learn …