ABSTRACT

Deep learning plays a major task in classification of unsupervised data, which utilises network enhanced learning. This technique proves to be powerful in remote sensing (RS) field for spatial data classification. In the existing environment, huge amounts of data from the earth observation satellites known as satellite images time series (SITS) are gathered, which can be utilised for observing the areas related to geography over through time. In this proposed model the time series model utilised is based on geography. There exists a challenge on how these types of information can be analysed in the field of remote sensing. Notable, techniques related to deep learning substantiated in dealing with remote sensing usually for classification of scene . In this paper, we propose an enhanced classification method involving Recurrent Neural Network (RNN) along with Random forest (RF) for land classification using satellite images, which are publicly available for various research purposes. We utilised spatial data gathered from the satellite images (i.e. time series). Our experimental classification is based on pixel and object-based classification. The attained analysis illustrates that the proposed model outperforms the other present day remote sensing classification techniques by producing 87% target accuracy of classification scene from satellite images.

摘要

深度学习在无监督数据分类中发挥着重要作用，它利用网络增强学习。事实证明，该技术在遥感 (RS) 领域中具有强大的空间数据分类功能。在现有环境中，收集了来自地球观测卫星的大量数据，称为卫星图像时间序列（SITS），可用于随时间推移观察与地理相关的区域。在这个提议的模型中，使用的时间序列模型基于地理。在遥感领域如何分析这些类型的信息存在挑战。值得注意的是，与深度学习相关的技术在处理遥感时得到证实，通常用于场景分类。在本文中，我们提出了一种涉及循环神经网络 (RNN) 和随机森林 (RF) 的增强分类方法，用于使用卫星图像进行土地分类，这些方法可公开用于各种研究目的。我们利用从卫星图像（即时间序列）收集的空间数据。我们的实验分类是基于像素和基于对象的分类。获得的分析表明，所提出的模型通过从卫星图像中产生 87% 的分类场景目标准确度，优于其他当今的遥感分类技术。我们的实验分类是基于像素和基于对象的分类。获得的分析表明，所提出的模型通过从卫星图像中产生 87% 的分类场景目标准确度，优于其他当今的遥感分类技术。我们的实验分类是基于像素和基于对象的分类。获得的分析表明，所提出的模型通过从卫星图像中产生 87% 的分类场景目标准确度，优于其他当今的遥感分类技术。