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2nd Edition of Recent Advances in Land Cover Classification and Change Detection in 2D and 3D

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 10575

Special Issue Editor

Dr. Chiman Kwan

E-Mail Website
Guest Editor
Signal Processing, Inc., Rockville, MD 20850-3563, USA
Interests: electronic nose; image demosacing; speech processing; image processing; remote sensing; deep learning; fault-tolerant control; fault diagnostics and prognostics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate digital surface models (DSMs) are important for many applications, including urban planning, land surveying before construction, urban change monitoring, etc. Lidar and radar have been widely used to obtain DSM information. Moreover, advances in stereo imaging using airborne and satellite imagers have also enabled the creation of DSM using optical images. At the same time, high-resolution color, multispectral (MS), and hyperspectral (HS) images are also available for land cover classification and change detection applications. Some airborne imagers can achieve centimeter resolutions, and satellite images also achieve sub-meter resolution. In this Special Issue, we aim at presenting the current state-of-the-art and most recent advances in land cover classification and change detection in 2D and 3D. Some practical applications are also included. Potential topics include:

  • DSM generation using airborne and satellite stereo imagers;
  • New technologies in Lidar and radar for DSM generation;
  • Land cover classification using optical, multispectral, and hyperspectral;
  • Land cover classification by fusing MS or HS images with DSM;
  • Change detection using optical, multispectral, and hyperspectral;
  • Change detection by fusing optical, MSI, and HIS images with DSM;
  • Digital terrain model (DTM) extraction by removing vegetation and human-made structures;
  • Damage assessment of landslides, hurricanes, earthquakes, tsunami, floods, etc. using SAR, optical, MS, and HS images.

Dr. Chiman Kwan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • DSM
  • DTM
  • Land cover classification
  • Change detection
  • Lidar
  • Stereo imaging
  • Optical
  • Multispectral
  • Hyperspectral

Published Papers (3 papers)

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Research

30 pages, 117086 KiB  
Article
Interoperability Study of Data Preprocessing for Deep Learning and High-Resolution Aerial Photographs for Forest and Vegetation Type Identification
by Feng-Cheng Lin and Yung-Chung Chuang
Remote Sens. 2021, 13(20), 4036; https://doi.org/10.3390/rs13204036 - 9 Oct 2021
Cited by 5 | Viewed by 2579
Abstract
When original aerial photographs are combined with deep learning to classify forest vegetation cover, these photographs are often hindered by the interlaced composition of complex backgrounds and vegetation types as well as the influence of different deep learning calculation processes, resulting in unpredictable [...] Read more.
When original aerial photographs are combined with deep learning to classify forest vegetation cover, these photographs are often hindered by the interlaced composition of complex backgrounds and vegetation types as well as the influence of different deep learning calculation processes, resulting in unpredictable training and test results. The purpose of this research is to evaluate (1) data preprocessing, (2) the number of classification targets, and (3) convolutional neural network (CNN) approaches combined with deep learning’s effects on high-resolution aerial photographs to identify forest and vegetation types. Data preprocessing is mainly composed of principal component analysis and content simplification (noise elimination). The number of classification targets is divided into 14 types of forest vegetation that are more complex and difficult to distinguish and seven types of forest vegetation that are simpler. We used CNN approaches to compare three CNN architectures: VGG19, ResNet50, and SegNet. This study found that the models had the best execution efficiency and classification accuracy after data preprocessing using principal component analysis. However, an increase in the number of classification targets significantly reduced the classification accuracy. The algorithm analysis showed that VGG19 achieved the best classification accuracy, but SegNet achieved the best performance and overall stability of relative convergence. This proves that data preprocessing helps identify forest and plant categories in aerial photographs with complex backgrounds. If combined with the appropriate CNN algorithm, these architectures will have great potential to replace high-cost on-site forestland surveys. At the end of this study, a user-friendly classification system for practical application is proposed, and its testing showed good results. Full article
(This article belongs to the Special Issue 2nd Edition of Recent Advances in Land Cover Classification and Change Detection in 2D and 3D)
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22 pages, 5140 KiB  
Article
Voxel Grid-Based Fast Registration of Terrestrial Point Cloud
by Biao Xiong, Weize Jiang, Dengke Li and Man Qi
Remote Sens. 2021, 13(10), 1905; https://doi.org/10.3390/rs13101905 - 13 May 2021
Cited by 23 | Viewed by 3852
Abstract
Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. [...] Read more.
Terrestrial laser scanning (TLS) is an important part of urban reconstruction and terrain surveying. In TLS applications, 4-point congruent set (4PCS) technology is widely used for the global registration of point clouds. However, TLS point clouds usually enjoy enormous data and uneven density. Obtaining the congruent set of tuples in a large point cloud scene can be challenging. To address this concern, we propose a registration method based on the voxel grid of the point cloud in this paper. First, we establish a voxel grid structure and index structure for the point cloud and eliminate uneven point cloud density. Then, based on the point cloud distribution in the voxel grid, keypoints are calculated to represent the entire point cloud. Fast query of voxel grids is used to restrict the selection of calculation points and filter out 4-point tuples on the same surface to reduce ambiguity in building registration. Finally, the voxel grid is used in our proposed approach to perform random queries of the array. Using different indoor and outdoor data to compare our proposed approach with other 4-point congruent set methods, according to the experimental results, in terms of registration efficiency, the proposed method is more than 50% higher than K4PCS and 78% higher than Super4PCS. Full article
(This article belongs to the Special Issue 2nd Edition of Recent Advances in Land Cover Classification and Change Detection in 2D and 3D)
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23 pages, 8186 KiB  
Article
Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area
by Carmen Valdivieso-Ros, Francisco Alonso-Sarria and Francisco Gomariz-Castillo
Remote Sens. 2021, 13(9), 1770; https://doi.org/10.3390/rs13091770 - 1 May 2021
Cited by 12 | Viewed by 3514
Abstract
Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from [...] Read more.
Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 ± 0.01 and 0.76 ± 0.01., but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes. Full article
(This article belongs to the Special Issue 2nd Edition of Recent Advances in Land Cover Classification and Change Detection in 2D and 3D)
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