MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03840nab|a22003257a|4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
65765 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20221207173120.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
20231s2023||||mx |||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
2352-9385 |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
https://doi.org/10.1016/j.rsase.2022.100859 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
MX-TxCIM |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mollick, T. |
| 9 (RLIN) |
10702 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc. |
Elsevier, |
| Date of publication, distribution, etc. |
2023. |
| Place of publication, distribution, etc. |
Amsterdam (Netherlands) : |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Peer review |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Reference only |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Bangladesh is primarily an agricultural country where technological advancement in the agricultural sector can ensure the acceleration of economic growth and ensure long-term food security. This research was conducted in the south-western coastal zone of Bangladesh, where rice is the main crop and other crops are also grown. Land use and land cover (LULC) classification using remote sensing techniques such as the use of satellite or unmanned aerial vehicle (UAV) images can forecast the crop yield and can also provide information on weeds, nutrient deficiencies, diseases, etc. to monitor and treat the crops. Depending on the reflectance received by sensors, remotely sensed images store a digital number (DN) for each pixel. Traditionally, these pixel values have been used to separate clusters and classify various objects. However, it frequently generates a lot of discontinuity in a particular land cover, resulting in small objects within a land cover that provide poor image classification output. It is called the salt-and-pepper effect. In order to classify land cover based on texture, shape, and neighbors, Pixel-Based Image Analysis (PBIA) and Object-Based Image Analysis (OBIA) methods use digital image classification algorithms like Maximum Likelihood (ML), K-Nearest Neighbors (KNN), k-means clustering algorithm, etc. to smooth this discontinuity. The authors evaluated the accuracy of both the PBIA and OBIA approaches by classifying the land cover of an agricultural field, taking into consideration the development of UAV technology and enhanced image resolution. For classifying multispectral UAV images, we used the KNN machine learning algorithm for object-based supervised image classification and Maximum Likelihood (ML) classification (parametric) for pixel-based supervised image classification. Whereas, for unsupervised classification using pixels, we used the K-means clustering technique. For image analysis, Near-infrared (NIR), Red (R), Green (G), and Blue (B) bands of a high-resolution ground sampling distance (GSD) 0.0125m UAV image was used in this research work. The study found that OBIA was 21% more accurate than PBIA, indicating 94.9% overall accuracy. In terms of Kappa statistics, OBIA was 27% more accurate than PBIA, indicating Kappa statistics accuracy of 93.4%. It indicates that OBIA provides better classification performance when compared to PBIA for the classification of high-resolution UAV images. This study found that by suggesting OBIA for more accurate identification of types of crops and land cover, which will help crop management, agricultural monitoring, and crop yield forecasting be more effective. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 591 ## - CATALOGING NOTES |
|---|
| Affiliation |
Mollick, T. : Not in IRS staff list but CIMMYT Affiliation |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Land cover mapping |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
28423 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
11127 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Unmanned aerial vehicles |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
11401 |
| 651 #7 - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME |
|---|
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1424 |
| Geographic name |
Bangladesh |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Azam, M.G. |
| 9 (RLIN) |
29380 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Karim, S. |
| 9 (RLIN) |
29381 |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Remote Sensing Applications: Society and Environment |
| Related parts |
v. 29, art. 100859 |
| Place, publisher, and date of publication |
Amsterdam (Netherlands) : Elsevier, 2023 |
| International Standard Serial Number |
2352-9385 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Article |
| Suppress in OPAC |
No |
| Source of classification or shelving scheme |
Dewey Decimal Classification |