scholarly journals Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8083
Author(s):  
Raoof Naushad ◽  
Tarunpreet Kaur ◽  
Ebrahim Ghaderpour
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Transfer Learning ◽  
Adaptive Learning ◽  
Data Augmentation ◽  
Learning Technologies ◽  
Computational Time ◽  
Spatiotemporal Information

Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide significant value in land use and land cover (LULC) classification. The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC classification. Moreover, diverse disciplines of science, including remote sensing, have utilised tremendous improvements in image classification involving convolutional neural networks (CNNs) with transfer learning. In this study, instead of training CNNs from scratch, the transfer learning was applied to fine-tune pre-trained networks Visual Geometry Group (VGG16) and Wide Residual Networks (WRNs), by replacing the final layers with additional layers, for LULC classification using the red–green–blue version of the EuroSAT dataset. Moreover, the performance and computational time are compared and optimised with techniques such as early stopping, gradient clipping, adaptive learning rates, and data augmentation. The proposed approaches have addressed the limited-data problem, and very good accuracies were achieved. The results show that the proposed method based on WRNs outperformed the previous best results in terms of computational efficiency and accuracy, by achieving 99.17%.

scholarly journals LAND USE AND LAND COVER MAPPING – A CASE STUDY OF AHMEDABAD DISTRICT

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 189-193
Author(s):  
D. Rawal ◽  
A. Chhabra ◽  
M. Pandya ◽  
A. Vyas
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Spectral Reflectance ◽  
Land Cover Classification ◽  
Land Cover Mapping ◽  
Classification Algorithms ◽  
Classification Technique ◽  
Object Based

Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflectance. Classification algorithms such as parallelepiped, minimum distance, maximum likelihood, Mahalanobis distance are some of the classification algorithms used in this technique. Other, Object based image classification is one of the most adapted land cover classification technique in recent time which also considers other parameters such as shape, colour, smoothness, compactness etc. apart from the spectral reflectance of single pixel.At present, there is a possibility of getting the more accurate information about the land cover classification by using latest technology, recent and relevant algorithms according to our study. In this study a combination of pixel-by-pixel image classification and object based image classification is done using different platforms like ArcGIS and e-cognition, respectively. The aim of the study is to analyze LULC pattern using satellite imagery and GIS for the Ahmedabad district in the state of Gujarat, India using a LISS-IV imagery acquired from January to April, 2017. The over-all accuracy of the classified map is 84.48% with Producer’s and User’s accuracy as 89.26% and 84.47% respectively. Kappa statistics for the classified map are calculated as 0.84. This classified map at 1:10,000 scale generated using recent available high resolution space borne data is a valuable input for various research studies over the study area and also provide useful information to town planners and civic authorities. The developed technique can be replicated for generating such LULC maps for other study areas as well.

scholarly journals Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2984
Author(s):  
Gyanendra Prasad Joshi ◽  
Fayadh Alenezi ◽  
Gopalakrishnan Thirumoorthy ◽  
Ashit Kumar Dutta ◽  
Jinsang You
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Data Augmentation ◽  
Land Cover Classification ◽  
Remote Sensing Image ◽  
Environmental Modeling ◽  
Classification Model ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

More accurate less meaningful? A critical physical geographer’s reflection on interpreting remote sensing land-use analyses

2021 ◽  
pp. 030913332199181
Author(s):  
Andreas Christian Braun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Geographic Information Systems ◽  
Image Classification ◽  
Satellite Image ◽  
Physical Geography ◽  
Data Availability ◽  
Scientific Results ◽  
Normative Considerations

Land-use and land-cover analyses based on satellite image classification are used in most, if not all, sub-disciplines of physical geography. Data availability and increasingly simple image classification techniques – nowadays, even implemented in simple geographic information systems – increase the use of such analyses. To assess the quality of such land-use analyses, accuracy metrics are applied. The results are considered to have sufficient quality, exceeding thresholds published in the literature. A typical practice in many studies is to confuse accuracy in remote sensing with quality, as required by physical geography. However, notions such as quality are subject to normative considerations and performative practices, which differ between scientific domains. Recent calls for critical physical geography have stressed that scientific results cannot be understood separately from the values and practices underlying them. This article critically discusses the specific understanding of quality in remote sensing, outlining norms and practices shaping it and their relation to physical geography. It points out that, as a seeming paradox, results considered more accurate in remote sensing terms can be less informative – or meaningful – in geographical terms. Finally, a roadmap of how to apply remote sensing land-use analyses more constructively in physical geography is proposed.

scholarly journals Land use land cover change in Kashmir Himalaya: Linking remote sensing with an indicator based DPSIR approach

2021 ◽  
Vol 125 ◽  
pp. 107447 ◽  
Author(s):  
Rehana Rasool ◽  
Abida Fayaz ◽  
Mifta ul Shafiq ◽  
Harmeet Singh ◽  
Pervez Ahmed
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Land Use Land Cover ◽  
Kashmir Himalaya ◽  
Dpsir Approach
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