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Leveraging Cloud Resources for Distributed Training of Residual CNNs in aerial image classification

IEEE ACCESS 2025年 13卷 31131-31139页

作者： Kumar, Shantanu Singh, Shruti Kumar Dewangan, Narendra Amazon Seattle WA 98109 USA Washington State Univ IREACH Seattle WA 98195 USA Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttaranchal India

aerial image classification is crucial across multiple sectors, including environmental monitoring, agriculture, and urban planning. However, processing large-scale aerial imagery efficiently poses challenges in model performance, computational efficiency, and scalability. This research introduces a novel convolutional neural network (CNN) architecture tailored for cactus identification from aerial photographs. The proposed cloud-based pipeline enhances training efficiency through scalable data storage, preprocessing, and distributed training across platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This comparative analysis demonstrates the model's computational efficiency, shorter training durations, and cost-effectiveness. The model integrates residual connections and depthwise separable convolutions, achieving 96.7% accuracy on the aerial cactus identification dataset. The results highlight the model's high performance, cost-efficiency, and scalability, making it suitable for real-world aerial image classification tasks. Future work aims to further optimize the model using advanced techniques and extend its application to multi-class classification challenges.

关键词： Cloud computing Computational modeling Training Artificial intelligence Data models image classification Computer architecture Computational efficiency Accuracy Scalability aerial image classification convolutional neural network (CNN) cloud computing distributed model training

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aerial image classification by learning quality-aware spatial pyramid model

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 111卷 271-277页

作者： Huang, Fenghua Qi, Xinjiu Li, Chuanlin Hu, Wei Yango Univ Spatial Data Min & Applicat Engn Res Ctr Fujian Prov Univ Fuzhou 350015 Peoples R China Yango Univ Coll Artificial Intelligence Fuzhou 350015 Peoples R China Fuzhou Univ Digital China Res Inst Fujian Fuzhou 350108 Peoples R China

Recognizing aerial image categories is of great significance in computer vision, which is widely utilized in geological analysis, agricultural production and urban planning. However, conventional approaches cannot explicitly exploit spatial information of aerial images, i.e., spatial relations among different components. To solve this problem, we propose a novel aerial image classification algorithm based on the well-known spatial pyramid model, where saliency maps are utilized for discriminative regions selection, and a visual quality model is leveraged to alleviate the impact of image distortion. More specifically, we first partition each aerial image into several fine subregions, each of which is represented using the computed spatial pyramid-based local features. Afterwards, each image is characterized by the most representative features engineered by the saliency map and quality assessment module. Subsequently, a regularized topic model based probabilistic learning is designed for recognizing different types of aerial images. Extensive experiments have demonstrated the effectiveness of our proposed method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： aerial image classification Quality model Spatial pyramid Saliency map

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aerial image classification through Thin Lensless Camera 5

Aerial Image Classification through Thin Lensless Camera

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IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR)

作者： Henry, Chris Kathariya, Birendra Asif, M. Salman Li, Zhu York, George Univ Missouri Kansas City Kansas City MO 64110 USA Univ Calif Riverside Riverside CA 92521 USA US Air Force Acad Colorado Springs CO 80841 USA

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

FlatCam - a lensless camera, is characterized by its thin form factor, flexibility, and low power consumption. These are the ideal characteristics for imaging system in an Unmanned aerial Vehicle. FlatCam captures the scene as sensor measurements and requires computational algorithms to reconstruct the scene. In this paper, we propose a reconstruction-free frequency domain deep learning based aerial image classification system. The system directly learns from sensor measurements thereby eliminating the computational cost associated with image reconstruction. The proposed approach computes the Discrete Cosine Transform of the sensor measurement and organizes it into subbands to learn an effective embedding for classification with a two-stage training using softmax and triplet loss. A triplet sampling strategy that uses label splitting is used for triplet loss learning. Our system is lightweight and the simulation results demonstrate its effectiveness.

关键词： Lensless camera FlatCam aerial image classification remote sensing discrete cosine transform

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aerial image classification with Label Splitting and Optimized Triplet Loss Learning

Aerial Image Classification with Label Splitting and Optimiz...

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IEEE International Conference on Visual Communications and image Processing (VCIP) - Visual Communications in the Era of AI and Limited Resources

作者： Liao, Rijun Li, Zhu Bhattacharyya, Shuvra S. York, George Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA Univ Maryland Dept Elect & Comp Engn College Pk MD USA Univ Maryland UMIACS College Pk MD 20742 USA US Air Force Acad Dept Elect & Comp Engn Colorado Springs CO USA US Air Force Acad UAS Res Ctr Colorado Springs CO 80840 USA

ISBN: (纸本)9781728185514

With the development of airplane platforms, aerial image classification plays an important role in a wide range of remote sensing applications. The number of most of aerial image dataset is very limited compared with other computer vision datasets. Unlike many works that use data augmentation to solve this problem, we adopt a novel strategy, called, label splitting, to deal with limited samples. Specifically, each sample has its original semantic label, we assign a new appearance label via unsupervised clustering for each sample by label splitting. Then an optimized triplet loss learning is applied to distill domain specific knowledge. This is achieved through a binary tree forest partitioning and triplets selection and optimization scheme that controls the triplet quality. Simulation results on NWPU, UCM and AID datasets demonstrate that proposed solution achieves the state-of-the-art performance in the aerial image classification.

关键词： aerial image classification Label Splitting Semantic Label Appearance Label Optimized Triplet Loss

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aerial image classification Using Structural Texture Similarity

Aerial Image Classification Using Structural Texture Similar...

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IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Risojevic, Vladimir Babic, Zdenka Univ Banja Luka Fac Elect Engn Banja Luka 78000 Bosnia & Herceg

ISBN: (纸本)9781467307536

There is an increasing need for algorithms for automatic analysis of remote sensing images and in this paper we address the problem of semantic classification of aerial images. For the task at hand we propose and evaluate local structural texture descriptor and similarity measure. Nearest neighbor classifier based on the proposed descriptor and similarity measure, as well as image-to-class similarity, improves classification rates over the state-of-the-art on two datasets of aerial images. We evaluate the design choices and show that rich subband statistics, perceptually-based structural texture similarity measure and image-to-class similarity all contribute to the good performance of our classifier.

关键词： aerial image classification Structural texture similarity Local image descriptors

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aerial image classification based on Sparse Representation and Deep Belief Network 35

Aerial Image Classification based on Sparse Representation a...

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第35届中国控制会议

作者： SHI Tao ZHANG Chunlei REN Hongge LI Fujin LIU Weimin College of Electrical Engineering North China University of Science and Technology

ISBN: (纸本)9781509009107

Due to the rapid development of satellite sensor technology,a rich aerial image data set can be easily *** to efficiently classify and recognize the aerial image has become a critical *** this paper,we propose an aerial image classification approachbased on sparse representation and deep belief network（DBN）.Low-level features are extracted by using scale-invariant feature transform（SIFT）.These extracted features are encoded in terms of an improved sparse encoding mode by combining structural similarity and sparse coding to generate new sparse *** is used to express the relationship between low-level feature and high-level semantic representation and complete image *** apply our approach to UC Merced data *** proposed approach obtained results that were equal to or even better than the previous results with the UC Merced data set.

关键词： aerial image classification sparse representation deep belief network feature extraction

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aerial image classification USING FUZZY MINER

AERIAL IMAGE CLASSIFICATION USING FUZZY MINER

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2nd International Conference on Advanced Computer Theory and Engineering (ICACTE 2009)

作者： Phokharatkul, Pisit Phaiboon, Supachai Mahidol Univ Fac Engn Dept Comp Engn Bangkok 10700 Thailand

ISBN: (纸本)9780791802977

aerial image classification is a method to classify and identify the objects on digital maps. Color, edge, shape, and texture have been extracted in order to classify objects on the aerial images. These feature attributes can be obtained directly from aerial images. However the complexity of data and number of rule based may be over information, which it can be reduced by the data mining techniques. In this research, we focus on the use of Fuzzy Logic for pattern classification. The attribute classifications have followed by the design and the implementation of its corresponding tool (Fuzzy Miner). Finally, the context of Fuzzy Miner is identified and to classify for its improvement are formulated. Extensive tests are performed to demonstrate the performance of Fuzzy Miner and compared with a performance Fuzzy C-Mean classifier. The results showed that, Fuzzy Miner has the best outcomes while Fuzzy C-Mean has the second rank outcomes.

关键词： aerial image classification Fuzzy Logic Fuzzy Miner Fuzzy C-Mean

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Unsupervised noise-robust feature extraction for aerial image classification

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Science China(Technological Sciences) 2020年第8期63卷 1406-1415页

作者： LIANG Ye LU Shuai WENG Rui HAN ChengZhe LIU Ming School of Astronautics Harbin Institute of TechnologyHarbin 150001China Academy of Art Harbin University of Science and TechnologyHarbin 150001China Academy of Software and Microelectronics Harbin University of Science and TechnologyHarbin 150001China

The rich data provided by satellites and unmanned aerial vehicles bring opportunities to directly model aerial image features by extracting their spatial and structural *** convolutional autoencoders(CAEs)have been attained a remarkable performance in ideal aerial image feature extraction,they are still challenging to extract information from noisy images which are generated from capture and *** this paper,a novel CAE-based noise-robust unsupervised learning method is proposed for extracting high-level features accurately from aerial images and mitigating the effect of *** from conventional CAEs,the proposed method introduces the noise-robust module between the encoder and the ***,several pooling layers in CAEs are replaced by convolutional layers with stride=*** performance of feature extraction is evaluated by the prediction accuracy and the accuracy loss in image classification experiments.A 5-classes aerial optical scene and a 9-classes hyperspectral image(HSI)data set are utilized for optical image and HSI feature extraction,*** features extracted from aerial images are utilized for image classification by a linear support vector machine(SVM)*** results indicate that the proposed method improves the classification accuracy for noisy images(Gaussian noise 2Dσ=0.1,3Dσ=60)in both optical images(2D 87.5%)and HSIs(3D 85.6%)compared with the traditional CAE(2D 78.6%,3D 84.2%).The accuracy loss in classification experiments increases with the increment of *** with the traditional CAE(2D 15.7%,3D 11.8%),the proposed method shows the lower classification accuracy loss in experiments(2D 0.3%,3D 6.3%).The proposed unsupervised noise-robust feature extraction method attains desirable classification accuracy in ideal input and enhances the feature extraction capability from noisy input.

关键词： aerial image classification convolutional autoencoder feature extraction noise-robust

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Pelican optimization algorithm with convolutional-recurrent hop field neural network for unmanned aerial image classification model

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第33期83卷 79029页

作者： Geetha, Nakkala Sunitha, Gurram Mohan Babu Univ Sch Comp Tirupati AP India

Nowadays, unmanned aerial vehicles (UAVs) becomes more prominent because of their benefits namely versatile, inexpensive, scalable, and autonomous. The aerial image classification procedure has involved further attention to the UAV-allowed surveillance model facilitating objective detection, and tracking procedure and significantly enhancing the visual surveillance outcomes. Earlier methods to resolve the scene classification method dependent upon feature coding systems with unsupervised feature learning or low-level hand-engineered features can make mid-level image features with limited representational capabilities that usually limit them from the achievement of effectual outcomes. The development of convolutional neural networks (CNNs) helps to attain an efficient classification process in the area of image classification. This paper introduces a Pelican Optimization Algorithm with Convolutional-Recurrent Hop-Field Neural Network (POA-CRHFNN) model for the UAV image classification model. The presented POA-CRHFNN method initially involves preprocessing in two stages namely Gaussian Filter (GF) based noise elimination and contrast enhancement. In addition, the POA-CRHFNN method exploits the ShuffleNetv2 CNN model for feature vector generation. For aerial image classification, the CRHFNN model is used, which allocates different types of classes to the aerial images. The combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in the CRHFNN can capture both spatial and temporal dependencies in aerial images. The integration of the Hopfield Neural Network (HNN) can recognize and classify aerial images based on learned patterns and associations. Finally, the POA is applied for fine-tuning the hyperparameters of the CRHFNN model. By leveraging the behavioural patterns of pelicans, the POA may explore the search space effectively and potentially find better hyperparameter configurations for improved classification performance. The effectiv

关键词： aerial image classification Deep learning Unmanned aerial vehicles Pelican optimization algorithm image processing

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Few-Shot aerial image classification with deep economic network and teacher knowledge

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2022年第13期43卷 5075-5099页

作者： Wang, Kang Wang, Xuesong Cheng, Yuhu China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Spac Minist Educ 1 Daxue Rd Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China

Deep learning has achieved excellent achievements and has become the mainstream in the field of aerial image classification. While obtaining remarkable success, deep learning-based approaches are notoriously dependent on large amounts of labelled data. Few-shot learning uses existing knowledge to learn from few samples and quickly generalizes to new tasks. In this work, we proposed the few-shot learning with deep economic network and teacher knowledge for aerial image classification. Firstly, we performed simplification twice to reduce large-scale parameters and computational effort in deep networks. In the first simplification, the redundancy in feature inputs' main components is reduced, and the implicit information in redundant components is extracted instead of directly discarding the redundant components. The channel and spatial redundancies in deeper layers' inputs are reduced in the second simplification. Secondly, the teacher knowledge guides the random sampling and uses limited samples to improve classification performance. We conducted extensive experiments on NWPU-RESISC45, RSD46-WHU, and UC Merced datasets. The experimental results reveal that the proposed method has better classification accuracy, fewer network parameters, and less computational effort. Experiments on miniimageNet, FC100, CUB, and cross-domain datasets show that our method also maintains advanced classification accuracy on few-shot image classification benchmarks.

关键词： Few-shot learning deep economic network teacher knowledge aerial image classification

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