{"title":"使用 DF-DNLSTM:深度特征双向长短期记忆模型对遥感图像进行高效分类","authors":"Monika Kumari, Ajay Kaul","doi":"10.1007/s13198-024-02466-w","DOIUrl":null,"url":null,"abstract":"<p>Scene classification in remote sensing is challenging due to high inter-class similarity and low intra-class similarity. Numerous techniques have been introduced, but accurately classifying scenes remains arduous. To address this challenge, To address this, we propose a hybrid framework, DF-DNLSTM, integrating DenseNet-121 for feature extraction and BiLSTM for sequential modeling, enhancing accuracy and contextual understanding. Second, a Conditional Generative Adversarial Network (CGAN) is employed for data augmentation, improving training data quantity and quality. Finally, the study introduces SwarmHawk, a hybrid optimization algorithm that combines particle swarm optimization (PSO) and Harris hawk optimization (HHO). SwarmHawk ensures the selection of informative features while concurrently eliminating duplicates and redundancies. It also reduces computational time to 4863 s. The proposed DF-DNLSTM model is rigorously assessed on three public datasets-UCM, AID, and NWPU. Results demonstrate its superior efficacy, achieving 99.87% accuracy on UCM, equivalent accuracy on NWPU, and sustaining 98.57% accuracy on AID. This study establishes DF-DNLSTM’s effectiveness, highlighting its potential contributions to advancing remote sensing scene classification.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient classification of remote sensing images using DF-DNLSTM: a deep feature densenet bidirectional long short term memory model\",\"authors\":\"Monika Kumari, Ajay Kaul\",\"doi\":\"10.1007/s13198-024-02466-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Scene classification in remote sensing is challenging due to high inter-class similarity and low intra-class similarity. Numerous techniques have been introduced, but accurately classifying scenes remains arduous. To address this challenge, To address this, we propose a hybrid framework, DF-DNLSTM, integrating DenseNet-121 for feature extraction and BiLSTM for sequential modeling, enhancing accuracy and contextual understanding. Second, a Conditional Generative Adversarial Network (CGAN) is employed for data augmentation, improving training data quantity and quality. Finally, the study introduces SwarmHawk, a hybrid optimization algorithm that combines particle swarm optimization (PSO) and Harris hawk optimization (HHO). SwarmHawk ensures the selection of informative features while concurrently eliminating duplicates and redundancies. It also reduces computational time to 4863 s. The proposed DF-DNLSTM model is rigorously assessed on three public datasets-UCM, AID, and NWPU. Results demonstrate its superior efficacy, achieving 99.87% accuracy on UCM, equivalent accuracy on NWPU, and sustaining 98.57% accuracy on AID. This study establishes DF-DNLSTM’s effectiveness, highlighting its potential contributions to advancing remote sensing scene classification.</p>\",\"PeriodicalId\":14463,\"journal\":{\"name\":\"International Journal of System Assurance Engineering and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of System Assurance Engineering and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s13198-024-02466-w\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02466-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Efficient classification of remote sensing images using DF-DNLSTM: a deep feature densenet bidirectional long short term memory model
Scene classification in remote sensing is challenging due to high inter-class similarity and low intra-class similarity. Numerous techniques have been introduced, but accurately classifying scenes remains arduous. To address this challenge, To address this, we propose a hybrid framework, DF-DNLSTM, integrating DenseNet-121 for feature extraction and BiLSTM for sequential modeling, enhancing accuracy and contextual understanding. Second, a Conditional Generative Adversarial Network (CGAN) is employed for data augmentation, improving training data quantity and quality. Finally, the study introduces SwarmHawk, a hybrid optimization algorithm that combines particle swarm optimization (PSO) and Harris hawk optimization (HHO). SwarmHawk ensures the selection of informative features while concurrently eliminating duplicates and redundancies. It also reduces computational time to 4863 s. The proposed DF-DNLSTM model is rigorously assessed on three public datasets-UCM, AID, and NWPU. Results demonstrate its superior efficacy, achieving 99.87% accuracy on UCM, equivalent accuracy on NWPU, and sustaining 98.57% accuracy on AID. This study establishes DF-DNLSTM’s effectiveness, highlighting its potential contributions to advancing remote sensing scene classification.
期刊介绍:
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.