Open Access

ARTICLE

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

Chengfan Li^1,2, Lan Liu^3,*, Junjuan Zhao¹, Xuefeng Liu⁴

1 School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
2 Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang, 330013, China
3 School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China
4 School of Communication & Information Engineering, Shanghai University, Shanghai, 200444, China

* Corresponding Author: Lan Liu. Email:

(This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)

Computer Modeling in Engineering & Sciences 2022, 131(1), 429-444. https://doi.org/10.32604/cmes.2022.019202

Received 09 September 2021; Accepted 12 October 2021; Issue published 24 January 2022

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.

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Keywords

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