LF-CNN: Deep Learning-Guided Small Sample Target Detection for Remote Sensing Classification

Chengfan Li,Junjuan Zhao,Lan Liu,Xuefeng Liu

doi:10.32604/cmes.2022.019202

Abstract

Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification probability of each category is then calculated and classified using the scaled expected linear units function and the full connection layer. The experimental results show that the proposed LF-CNN method has a high accuracy of target detection and classification for hyperspectral imager remote sensing data under the condition of small samples. Despite drawbacks in both time and complexity, the proposed LF-CNN method can more effectively integrate the local features of ground object samples and improve the accuracy of target identification and detection in small samples of remote sensing images than traditional target detection methods.

Highlights

We propose a small sample target detection method for the classification of remote sensing images that combines local features with convolutional neural network (CNN)
(2) We propose the detection of small samples from remote sensing images based on CNN with local features (LF-CNN)
This shows that the proposed local features and a convolutional neural network (LF-CNN) method can effectively detect small sample targets from remote sensing images

Summary

Introduction

With the continuous development of satellite-borne sensors, remote sensing technology has become an important means of surveying the Earth’s resources and monitoring the ecological environment, with ever-increasing fields of application [1–3]. Remote sensing images contain many different types of targets and these targets are unevenly distributed. It is difficult to establish databases of target images as a result of these small sample sizes, especially in remote areas with poor access—for example, there are usually only a few images of targets in the oceans and in. It is difficult to obtain effective training samples for target detection in remote sensing images and the distribution of different types of targets is unbalanced. The color of targets in remote sensing images is often mixed with the background and the size of the targets varies greatly, which leads to weakening of the target’s features in the camera’s field of view. It is important to realize the robust discrimination of target types when detecting the features of target information from remote sensing images with small samples using deep learning theory

Methods

Results

Conclusion