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A coupled multi-task feature boosting method for remote sensing scene classification

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Science China(Technological Sciences) 2023年第3期66卷 663-673页

作者： WANG TengFei GU YanFeng GAO GuoMing ZENG XiaoPeng School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The scene classification plays an essential role in processing very high resolution(VHR)images for understanding.The scene classification in remote sensing faces two difficulties:the mismatching features caused by the model overfitting problem and the semantic information losing problem.The multi-task method helps solve the problems by using the share weights of multiply tasks.We propose a feature boosting method with a multi-task framework that combines the scene classification task and the semantic segmentation task to overcome the difficulties.Different from the traditional multi-task learning method,the two tasks are coupled together via a weakly supervised learning method so that it does not require the labelled semantic segmentation samples.First,we proposed a weakly supervised segmentation method to create the interconnection of the segmentation task and the classification task.And we achieve a coarse segmentation result which is highly correlated to the classification by the weakly supervised method.Second,according to the surface distribution of remote sensing,we propose a sparse surface constraint to obtain fine segmentation results.Fine features are obtained by constraining the shared weights of the weakly supervised segmentation method.Last,we classify the scenes using the fine features and conduct experiments on the public remote sensing scene classification datasets.Experimental results demonstrate that the proposed coupled multi-task model outperforms the stateof-the-art methods on remote sensing scene classification.

关键词： scene classification coupled multi-task feature boosting

Intelligent Deep Data Analytics Based Remote Sensing scene classification Model

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Computers, Materials & Continua 2022年第7期72卷 1921-1938页

作者： Ahmed Althobaiti Abdullah Alhumaidi Alotaibi Sayed Abdel-Khalek Suliman A.Alsuhibany Romany F.Mansour Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Science and Technology College of RanyahTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics College of ScienceP.O.Box 11099Taif UniversityTaif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate change.UAVs have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision making.Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs.This paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)model.The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels.The MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)models.This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance.In addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization technique.The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of neurons.Besides,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature vectors.The proposedMMF-EDCmodel has been tested using UCM and WHU-RS datasets.The proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.

关键词： Remote sensing unmanned aerial vehicles deep learning artificial intelligence scene classification

Adaptive Binary Coding for scene classification Based on Convolutional Networks

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Computers, Materials & Continua 2020年第12期65卷 2065-2077页

作者： Shuai Wang Xianyi Chen Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel HillChapel HillUSA Mathematics and Computer Science University of North Carolina at PembrokePembrokeUSA

With the rapid development of computer technology,millions of images are produced everyday by different sources.How to efficiently process these images and accurately discern the scene in them becomes an important but tough task.In this paper,we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification.Specifically,we first extract some high-level features of images under consideration based on available models trained on public datasets.Then,we further design a binary encoding method called one-hot encoding to make the feature representation more efficient.Benefiting from the proposed adaptive binary coding,our method is free of time to train or fine-tune the deep network and can effectively handle different applications.Experimental results on three public datasets,i.e.,UIUC sports event dataset,MIT Indoor dataset,and UC Merced dataset in terms of three different classifiers,demonstrate that our method is superior to the state-of-the-art methods with large margins.

关键词： scene classification convolutional neural network one-hot encoding supervised feature training

A More Efficient Approach for Remote Sensing Image classification

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Computers, Materials & Continua 2023年第3期74卷 5741-5756页

作者： Huaxiang Song School of Geography Science and Tourism Hunan University of Arts and ScienceChangde415000China

Over the past decade,the significant growth of the convolutional neural network(CNN)based on deep learning(DL)approaches has greatly improved the machine learning(ML)algorithm’s performance on the semantic scene classification(SSC)of remote sensing images(RSI).However,the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms,e.g.,automation and simplicity,partially lost.Traditional ML strategies(e.g.,the handcrafted features or indicators)and accuracy-aimed strategies with a high trade-off(e.g.,the multi-stage CNNs and ensemble of multi-CNNs)are widely used without any training efficiency optimization involved,which may result in suboptimal performance.To address this problem,we propose a fast and simple training CNN framework(named FST-EfficientNet)for RSI-SSC based on an EfficientNetversion2 small(EfficientNetV2-S)CNN model.The whole algorithm flow is completely one-stage and end-to-end without any handcrafted features or discriminators introduced.In the implementation of training efficiency optimization,only several routine data augmentation tricks coupled with a fixed ratio of resolution or a gradually increasing resolution strategy are employed,so that the algorithm’s trade-off is very cheap.The performance evaluation shows that our FST-EfficientNet achieves new state-of-the-art(SOTA)records in the overall accuracy(OA)with about 0.8%to 2.7%ahead of all earlier methods on the Aerial Image Dataset(AID)and Northwestern Poly-technical University Remote Sensing Image scene classification 45 Dataset(NWPU-RESISC45D).Meanwhile,the results also demonstrate the importance and indispensability of training efficiency optimization strategies for RSI-SSC by DL.In fact,it is not necessary to gain better classification accuracy by completely relying on an excessive trade-off without efficiency.Ultimately,these findings are expected to contribute to the development of more efficient CNN-based approaches in RSI-SSC.

关键词： FST-EfficientNet efficient approach scene classification remote sensing deep learning

Remote Sensing Image scene classification via Multi-feature Fusion

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Remote Sensing Image Scene Classification via Multi-feature ...

Bridging the semantic gap with human perception based features for scene categorization

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作者： Ruiyao Liu Xiaoyong Bian Yuxia Sheng School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Information Science and Engineenring Wuhan University of Science and Technology

In this papers, classification of remote sensing image scene is investigated. A scene classification approach based on multi-feature fusion has been proposed. In the proposed approach, three types of features are extracted. Specifically, extended multi-attribute profile（EMAP）-based texture feature, saliency-based shape feature and color ones. The texture features are extracted by EMAP. Furthermore, the Hu invariant moments are extracted from the saliency map, where the saliency map is obtained by frequency-tuned saliency detection. Meanwhile, the color moments are extracted as the color features from the image scenes. As for EMAP-based features, dimension reduction via principal component analysis（PCA） is first performed and combined with other two types of features to form a compact feature representation. Finally, support vector machine（SVM） is employed to classify the remote sensing image scenes. The experiments on the two challenging image scene datasets are performed to show that the proposed method is simple, yet efficient to implement, comparing with the state-of-the-arts.

关键词： scene classification morphological attribute profile saliency map feature extraction

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International Journal of Intelligent Computing and Cybernetics 2017年第3期10卷 387-406页

作者： Padmavati Shrivastava K.K.Bhoyar A.S.Zadgaonkar Dr.C.V.Raman University BilaspurIndia Department of Information Technology Yeshwantrao Chavan College of EngineeringNagpurIndia

Purpose–The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision,in gathering knowledge about the structure,content and the surrounding environment of a real-world natural scene,at a quick glance accurately.This paper proposes a set of novel features to determine the gist of a given scene based on dominant color,dominant direction,openness and roughness features.Design/methodology/approach–The classification system is designed at two different levels.At the first level,a set of low level features are extracted for each semantic feature.At the second level the extracted features are subjected to the process of feature evaluation,based on inter-class and intra-class distances.The most discriminating features are retained and used for training the support vector machine(SVM)classifier for two different data sets.Findings–Accuracy of the proposed system has been evaluated on two data sets:the well-known Oliva-Torralba data set and the customized image data set comprising of high-resolution images of natural landscapes.The experimentation on these two data sets with the proposed novel feature set and SVM classifier has provided 92.68 percent average classification accuracy,using ten-fold cross validation approach.The set of proposed features efficiently represent visual information and are therefore capable of narrowing the semantic gap between low-level image representation and high-level human perception.Originality/value–The method presented in this paper represents a new approach for extracting low-level features of reduced dimensionality that is able to model human perception for the task of scene classification.The methods of mapping primitive features to high-level features are intuitive to the user and are capable of reducing the semantic gap.The proposed feature evaluation technique is general and can be applied across any domain.

关键词： Openness Roughness Human perception JND p-value scene classification Semantic gap Feature elevation Dominant direction