Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

Kang, Miao; Ji, Kefeng; Leng, Xiangguang; Xing, Xiangwei; Zou, Huanxin

doi:10.3390/s17010192

Cited by 98 publications

(66 citation statements)

References 31 publications

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“…In particular, they convert raw data to an abstract representation using a simple non-linear model and they integrate features using an optimization algorithm. This results in a substantial decrease of redundant information between the features while achieving a strong generalization capacity [35].…”

Section: Introductionmentioning

confidence: 99%

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

et al. 2018

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Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

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Section: Introductionmentioning

confidence: 99%

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

et al. 2018

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“…Feature extraction plays an important role in both target recognition and tracking. The traditional hand-crafted features have been used in various modalities images [32]. Over the past few years, DCNN-based method has outperformed the traditional approaches in various computer vision domains, such as image classification, target recognition, and semantic segmentation, because of the strong ability of feature extraction.…”

Section: Dcnn-based Target Recognitionmentioning

confidence: 99%

Target Recognition in Infrared Circumferential Scanning System via Deep Convolutional Neural Networks

Chen

2020

Sensors

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With an infrared circumferential scanning system (IRCSS), we can realize long-time surveillance over a large field of view. Recognizing targets in the field of view automatically is a crucial component of improving environmental awareness under the trend of informatization, especially in the defense system. Target recognition consists of two subtasks: detection and identification, corresponding to the position and category of the target, respectively. In this study, we propose a deep convolutional neural network (DCNN)-based method to realize the end-to-end target recognition in the IRCSS. Existing DCNN-based methods require a large annotated dataset for training, while public infrared datasets are mostly used for target tracking. Therefore, we build an infrared target recognition dataset to both overcome the shortage of data and enhance the adaptability of the algorithm in various scenes. We then use data augmentation and exploit the optimal cross-domain transfer learning strategy for network training. In this process, we design the smoother L1 as the loss function in bounding box regression for better localization performance. In the experiments, the proposed method achieved 82.7 mAP, accomplishing the end-to-end infrared target recognition with high effectiveness on accuracy.

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“…Furthermore, there are some traditional methods generally used to extract features of HS images to represent the spatial information. It is not satisfying that most of them are shallow features that can not fully express the comprehensive information of HS images [53][54][55]. To solve the problem, in this paper, we propose the SCAAE based pansharpening method to mine deeper features.…”

Section: Feature Extractionmentioning

confidence: 99%

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

Zhong

Lei

et al. 2019

Remote Sensing

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Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.

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Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

Cited by 98 publications

References 31 publications

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Target Recognition in Infrared Circumferential Scanning System via Deep Convolutional Neural Networks

Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

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