Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images

2019 ◽  
Vol 47 (11) ◽  
pp. 1949-1958 ◽  
Author(s):  
Kavita Bhosle ◽  
Vijaya Musande
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
Land Cover ◽  
Land Cover Classification ◽  
Hyperspectral Remote Sensing ◽  
Land Use Land Cover ◽  
Remote Sensing Images ◽  
Hyperspectral Remote Sensing Images

scholarly journals Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images

2016 ◽  
Vol XV (1) ◽  
pp. 45-53 ◽  
Author(s):  
Varun Narayan Mishra ◽  
Praveen Kumar Rai ◽  
Pradeep Kumar ◽  
Rajendra Prasad
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Land Use Land Cover ◽  
Remote Sensing Images

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

2020 ◽  
Vol 11 (5) ◽  
pp. 529-535
Author(s):  
Dan Abudu ◽  
Nigar Sultana Parvin ◽  
Geoffrey Andogah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Land Use Changes ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Sensing Technology

Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor’s spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare- and vegetation-covers, limits Remote Sensing ability to accurately discriminate urban features. The launch of Sentinel-1 Synthetic Aperture Radar, which operates at metric resolution and microwave frequencies evades the weather limitations and has been reported to accurately quantify urban compositions. This paper assessed the feasibility of Sentinel-1 SAR data for urban land use land cover classification by reviewing research papers that utilised these data. The review found that since 2014, 11 studies have specifically utilised the datasets.

scholarly journals LAND USE LAND COVER CLASSIFICATION AND WHEAT YIELD PREDICTION IN THE LOWER CHENAB CANAL SYSTEM USING REMOTE SENSING AND GIS

2020 ◽  
Vol 2 (2) ◽  
pp. 47-51
Author(s):  
Aftab Nazeer ◽  
Muhammad Mohsin Waqas ◽  
Sikandar Ali ◽  
Usman Khalid Awan ◽  
Muhammad Jehanzeb Masu Cheema ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Wheat Yield ◽  
Land Cover Classification ◽  
Remote Sensing And Gis ◽  
Yield Prediction ◽  
Land Use Land Cover ◽  
Canal System

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.

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