研究目的
To analyze and predict urban sprawl changes in Salem city using satellite images and image processing techniques.
研究成果
The use of maximum likelihood classifier technique provides better results for analyzing changes in built-up land in Salem city. The study shows significant urban expansion from 1973 to 2016, with built-up area increasing from 11.9 Sq.km to 55.76 Sq.km. Agriculture and forest areas have significantly decreased, indicating a need for sustainable urban planning.
研究不足
The study is limited to Salem city and may not be directly applicable to other urban areas without adjustments. The accuracy of classification depends on the quality of satellite images and the effectiveness of the image processing techniques used.
1：Experimental Design and Method Selection:
The study uses histogram equalization for image enhancement and maximum likelihood classification for feature extraction and classification.
2：Sample Selection and Data Sources:
Satellite images of Salem city from 1973 to 2016 were taken from USGS Earth Explorer.
3：List of Experimental Equipment and Materials:
Landsat satellite images, GIS tools.
4：Experimental Procedures and Operational Workflow:
Preprocessing with histogram equalization, feature extraction using signature method, classification with maximum likelihood technique, and change detection analysis.
5：Data Analysis Methods:
Quantitative assessment of land changes using GIS and remote sensing tools.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
