scholarly journals A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research

2016 ◽  
Vol 177 ◽  
pp. 89-100 ◽  
Author(s):  
Reza Khatami ◽  
Giorgos Mountrakis ◽  
Stephen V. Stehman
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Meta Analysis ◽  
Cover Image ◽  
Future Research

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 REMOTE SENSING IMAGE CLASSIFICATION USING ARTIFICIAL BEE COLONY ALGORITHM

2014 ◽  
pp. 155-160
Author(s):  
SRIDEEPA BANERJEE ◽  
AKANKSHA BHARADWAJ ◽  
DAYA GUPTA ◽  
V.K. PANCHAL
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Artificial Bee Colony ◽  
Spatial Information ◽  
Satellite Image ◽  
Fuzzy Classifier ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Remote Sensing has been globally used for knowledge elicitation of earth’s surface and atmosphere. Land cover mapping, one of the widely used applications of remote sensing is a method for acquiring geo-spatial information from satellite data. We have attempted here to solve the land cover problem by image classification using one of the newest and most promising Swarm techniques of Artificial Bee Colony optimization (ABC). In this paper we propose an implementation of ABC for satellite image classification. ABC is used for optimal classification of images for mapping the land-usage efficiently. The results produced by ABC algorithm are compared with the results obtained by other techniques like BBO, MLC, MDC, Membrane computing and Fuzzy classifier to show the effectiveness of our proposed implementation.

scholarly journals UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 46 ◽  
Author(s):  
Efstathios Adamopoulos ◽  
Fulvio Rinaudo
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Meta Analysis ◽  
Visible Spectrum ◽  
Unmanned Aircraft ◽  
Future Research ◽  
Radar Sensors ◽  
Technological Developments ◽  
Aerial Platforms ◽  
Historical Sites

Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research.

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 A review of supervised object-based land-cover image classification

2017 ◽  
Vol 130 ◽  
pp. 277-293 ◽  
Author(s):  
Lei Ma ◽  
Manchun Li ◽  
Xiaoxue Ma ◽  
Liang Cheng ◽  
Peijun Du ◽  
...  
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Cover Image ◽  
Object Based

Object-Oriented Land Cover Image Classification System

2010 ◽  
Vol 4 (1) ◽  
pp. 56-62 ◽  
Author(s):  
Yun Yongsheng ◽  
Chang Qingrui ◽  
Xie Jing
Keyword(s):  
Land Cover ◽  
Image Classification ◽  
Classification System ◽  
Object Oriented ◽  
Cover Image

scholarly journals Classification of Land Cover from Remote Sensing Images using Morphological Linear Contact Distributions and Rough Sets

2019 ◽  
Vol 8 (3) ◽  
pp. 676-688
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Image Classification ◽  
Rough Sets ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Classification Algorithm ◽  
Forest Monitoring ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Remote sensing image classification plays an essential role in computer vision and image processing to address the problems in the areas of agriculture, forest monitoring, urban development, environment protection, etc. A lot of literature is available on remote sensing image classification. But, it is still a research task even today because of the multitude of problems. RTBFCA (Rough Texture Based Features Classification Algorithm), a new classification algorithm has been proposed in this paper. This paper aims at classifying the remote sensing images into various cover types using mathematical morphology and rough sets. Morphological texture features (linear contact distributions) along with first order statistics are used to identify the pixels of various classes and the concepts of lower and upper approximations of rough sets are used for clustering the features of the pixels and then are finally classified to display the classified image. The proposed method was tested on Google Earth images and is able to classify even various crops patterns of a land cover image. The algorithm is compared with other algorithms like ”GLCM with rough sets”, ”intensity values with rough sets” and with ”linear contact distributions with rough sets”.

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