CNN-LandCoverNet:使用混合元启发式辅助基于集合的卷积神经网络进行土地覆被分类的有效框架

IF 0.8 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Samrajam Jyothula, S. Chandrasekhar
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引用次数: 0

摘要

土地覆被分类被认为是遥感图像智能解译技术的一项必要任务,其目的是对每个像素进行分类,以执行预定义的土地覆被分类。土地利用和土地覆盖(LULC)信息能够提供各种见解,以克服环境和社会经济影响,如灾害风险、气候变化、贫困和粮食不安全。因此,传统工作中涉及到图像分类任务,其中经典的视觉解读技术完全依赖于专业知识以及专业人员的分类经验,更容易受到主观意识的影响,效率低下且耗时。为了克服这一问题,本文提出了一种最新的深度结构方法来进行土地退化图像分类。首先,收集土地图像。然后,利用收集到的图像进行斑块分割,将图像分割成多个斑块。分割完成后,这些斑块被输送到基于集合的卷积神经网络(ECNN),该网络由全卷积网络(FCN)、U-Net、DeepLabv3 和基于掩码区域的卷积神经网络(Mask R-CNN)构建而成,用于执行分割。在此,通过将台球启发优化算法(BOA)和水波算法(WWA)进行整合,利用混合台球启发水波算法(HB-WWA)对超参数进行优化调整。最后,使用模糊分类器进行分类。因此,通过不同的指标对性能进行了验证和测量。因此,在对其他现有算法进行测试时,所开发的工作显示出更高的分类准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN-LandCoverNet: An Effective Framework of Land Cover Classification Using Hybrid Metaheuristic-Aided Ensemble-Based Convolutional Neural Network
Land cover (LC) categorization is considered a necessary task of intelligent interpretation technology for remote sensing imagery that is intended to categorize every pixel to perform the predefined LC classification. Land Use and Land Cover (LULC) information has the ability to provide various insights in order to overcome environmental and socioeconomic impacts such as disaster risk, climate change, poverty, and food insecurity. Therefore, image categorization tasks are involved in conventional works, where the classical visual interpretation techniques completely depend upon professional knowledge as well as a professional’s classification experience, which is more susceptible to subjective awareness, inefficient, and time consuming. By overcoming this issue, the latest deep-structured approach is suggested to perform the LC image classification. Initially, the land images are gathered. Further, the collected images are employed for patch splitting, where the images are split into multiple patches. After splitting, the patches are fed to the Ensemble-based Convolutional Neural Network (ECNN), which is constructed with a Fully Convolutional Network (FCN), U-Net, DeepLabv3, and Mask Region-based Convolutional Neural Network (Mask R-CNN) for performing segmentation. Here, the hyperparameters are optimally tuned with the Hybrid Billiards-inspired Water Wave Algorithm (HB-WWA) by integrating the Billiards-inspired Optimization Algorithm (BOA) and Water Wave Algorithm (WWA). Finally, the classification is carried out with a fuzzy classifier. Thus, the performance is validated and measured through diverse metrics. Consequently, the developed work has demonstrated enhanced classification accuracy when tested on other existing algorithms.
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来源期刊
International Journal of Image and Graphics
International Journal of Image and Graphics COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
18.80%
发文量
67
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