.png){kind=icon}
.png){kind=icon}
Return
Multilayer Feature Extraction Object Detection Based on Deep Forest
.png){kind=icon}
OA
AI
DOI：10.1109/JSTARS.2025.3622047.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the emergence of various large-scale deep-learning models, in remote sensing images, the object detection effect is also plagued by complex calculations, high costs, and high requirements for datasets and parameters. Based on simple shallow structures, it is impossible to complete more abstract feature expressions, especially the extraction and fusion of multilevel information. Considering together three key factors affecting deep-learning models: layer-by-layer processing, deep feature information extraction, and dependence on datasets. This article proposes a multilayer feature extraction object detection method based on deep forest (MLFEDF). The backbone network uses a basic structure to extract multiscale features, and we design a spatial position information processing module to reduce information loss. Then, the feature refinement process from shallow to deep layers is designed to achieve multiscale refined object representation. Finally, drawing on the idea of deep forest, multilevel features are jointly utilized to complete the final classification regression. Our model is compared with the existing seven object detection models on the DOTA dataset. The experimental results show that the mAP detection index of MLFEDF reaches 79.15%, which is better than other networks. At the same time, the discussion experiments on parameters and small datasets show that our model can reduce the model’s requirements on datasets and parameters, and the accuracy loss is only 3.69% .
Keywords:
Deep forest
multilayer feature extraction
object detection
remote sensing (RS) image
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.3
Papers:
1.3K
Citations:
3.0W
Organization
View more organizations