Research on Land Use Planning Based on Multisource Remote Sensing Data

Abstract

Land use changes are analyzed correctly, a series of improvements according to the changes are carried out appropriately, the relationship between land use development and economic and human survival is handled correctly, and the healthy and orderly development of the entire society is promoted. Aiming at the combination of multisource remote sensing data and monitoring changes in land planning, this study uses CBERS data and ASAR data as multisource remote sensing data sources to conduct in-depth research and discussion on the land use change in this area and uses the HPF pixel-level fusion method for data fusion to generate HPF. The data are integrated, and then, the CBERS data and HPF fusion data are used to extract the land use type information of Zhenning County, respectively, and a confusion matrix is built based on the field sample points to verify the accuracy, compare and analyze the relative error of the land use type information extraction before and after data fusion, and evaluate the CBERS data. Regarding the extraction effect of land use type information of fusion data with HPF, the results show that the two kinds of remote sensing data have good effects in extracting water body type information, and the accuracy has reached 100%. Using multisource remote sensing image processing can well summarize and analyze land use changes and make the changes in various indicators in the study area. Accurate statistics is obtained.

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia

“…Multispectral sensors and geographical data have increased in availability during the past four decades. [ 22 ] To obtain the most details about the area being classified, it is preferable to use multisource data when classifying land cover from remote sensing data. [ 25 , 26 ] These days, there is a large availability of satellites on the Earth's surface and classification algorithms.…”

“…[8,18,20,21] Multisource remote sensing and geographical data have increased in availability during the past four decades. [22] Multisource data, as the name suggests, combines information from various sources. Examples include radar data, multispectral images from the Landsat satellite, hyperspectral airborne data, and geographical information like elevation and slope.…”

Land Cover Classification of SAR Based on 1DCNN-MRF Model Using Improved Dual-Polarization Radar Vegetation Index

“…The specific imaging parameters are shown in Table 1. In this study, the international SAR data processing software PIE-SAR 6.3, which was developed in China, was used to process the GF-3 data [51], specifically: (1) orbit file application; (2) calibration; the backscatter amplitude information on the different polarization channels was corrected according to the calibration constants in the header file to obtain the backscatter coefficient for each pixel; (3) generate polarized covariance matrix; (4) multi-looking; (5) refined Lee filtering; a 3 × 3 fine Lee filtering was used to reduce the influence of speckle noise; (6) polarization decomposition; (7) range Doppler terrain correction; (8) conversion of the backscatter coefficient from a linear to a dB scale; (9) reprojection; (10) study area extraction.…”

Compression and encryption for remote sensing image based on PSO-BP and 2D-MCCM

“…They enable the acquisition and recording of information about target objects from a distance. Remote sensing is commonly used in fields such as climate detection [1], land use [2], and disaster management [3]. Remote sensing images are characterized by large data volumes.…”