# Adaptive Remote Sensing Image Attribute Learning for Active Object Detection

Source: [Adaptive Remote Sensing Image Attribute Learning for Active Object Detection](<https://www.sota2.com/research/paper/68fa691a1e209131cea203e8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:53:51.580Z

Authors: Nuo Xu, Chunlei Huo, Jiacheng Guo, Yiwei Liu, Jian Wang, Chunhong Pan

Published: 2021-01-16T11:37:50.000Z

## Abstract

In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglect the relationship between imaging configuration and detection performance, and do not take into account the importance of detection performance feedback for improving image quality. Therefore, detection performance is limited by the passive nature of the conventional object detection framework. In order to solve the above limitations, this paper takes adaptive brightness adjustment and scale adjustment as examples, and proposes an active object detection method based on deep reinforcement learning. The goal of adaptive image attribute learning is to maximize the detection performance. With the help of active object detection and image attribute adjustment strategies, low-quality images can be converted into high-quality images, and the overall performance is improved without retraining the detector.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.06438>)

[PDF](<https://arxiv.org/pdf/2101.06438.pdf>)