Multi-spectral remote sensing land-cover classification based on deep learning methods

2020 ◽  
Author(s):  
Tongdi He ◽  
Shengxin Wang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Land Cover Classification ◽  
Learning Methods

scholarly journals Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta

2019 ◽  
Vol 11 (9) ◽  
pp. 1006 ◽  
Author(s):  
Quanlong Feng ◽  
Jianyu Yang ◽  
Dehai Zhu ◽  
Jiantao Liu ◽  
Hao Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Yellow River ◽  
Rapid Development ◽  
Land Cover Classification ◽  
Coastal Land

Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

scholarly journals Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

2020 ◽  
Vol 12 (15) ◽  
pp. 2495 ◽  
Author(s):  
Ava Vali ◽  
Sara Comai ◽  
Matteo Matteucci
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
Land Cover ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Global Scale ◽  
Hyperspectral Images ◽  
Machine Learning Techniques ◽  
Observation Data

Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

scholarly journals How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?

2020 ◽  
Vol 12 (3) ◽  
pp. 417 ◽  
Author(s):  
Xin Zhang ◽  
Liangxiu Han ◽  
Lianghao Han ◽  
Liang Zhu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Object Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Sensing Data

Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models.

scholarly journals LAND COVER CLASSIFICATION USING HIGH RESOLUTION SATELLITE IMAGE BASED ON DEEP LEARNING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 685-690
Author(s):  
M. Zhu ◽  
B. Wu ◽  
Y. N. He ◽  
Y. Q. He
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Land Cover Classification ◽  
Main Task ◽  
High Resolution Satellite Image

Abstract. In the coming era of big data, the high resolution satellite image plays an important role in providing a rich source of information for a variety of applications. Land cover classification is a major field of remote sensing application. The main task of land cover classification is to divide the pixels or regions in remote sensing imagery into several categories according to application requirements. Recently, machine interpretation methods including artificial neural network and decision tree are developing rapidly with certain fruits achieved. Compared with traditional methods, deep learning is completely data-driven, which can automatically find the best ways to extract land cover features through high resolution satellite image. This study presents a detailed investigation of convolutional neural networks for the classification of complex land cover classes using high resolution satellite image. The main contributions of this paper are as follows: (1) Aiming at the uneven spatial distribution of surface coverage, we study the training errors caused by this uneven distribution. An improved SMOTE algorithm is designed for automatic processing the task of sample augmentation. Through experimental verification, the improver algorithm can increase 2–5% classification accuracy by the same network structure. (2) The main representations of the network are also shared between the edge loss reinforced structures and semantic segmentation, which means that the CNN simultaneously achieves semantic segmentation by edge detection. (3) We use Beijing-2 satellite (BJ-2) remote sensing data for training and evaluation with Integrated Model, and the total accuracy reaches 89.6%.

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