Vol. 1 No. 1 (2012)

Open Access

Article

Article ID: 273

based on Landsat 8 Remote sensing image of the northern region of Changchun

by yi Ma yi Ma

Remote Sensing, Vol.1, No.1, 2024; 118 Views, 11 PDF Downloads

with Landsat 8 Remote sensing image to data source , using flaash atmospheric Correction Model Atmospheric correction of remote sensing images ,Combining field soil sampling Organic matter Assay Data , using stepwise regression analysis , quantitative inversion of soil organic matter content in the study area . results show , Soil organic matter content with Landsat 8 Remote sensing image reflectivity has strong negative correlation in near infrared band , An appropriate mathematical transformation of the reflectivity can effectively increase the correlation with the organic matter , The stepwise regression model established by this method , Its decision factor r2 =0. 925, Total root Variance Flmse =0. 171, describes the inverse The model has higher precision and stability . based on the above inversion model , Combining remote sensing image classification results , inversion of soil organic matter content in the study area , knot to show , content of soil organic matter in the study area showed a tendency of East High west , East , The content of soil organic matter in the South is generally higher than that of 3, and West , North Region Soil organic matter content is generally lower than 2%.

Open Access

Article

Article ID: 249

Application of BS-GEP algorithm in Remote sensing Image classification

by dan Lin dan Lin

Remote Sensing, Vol.1, No.1, 2024; 181 Views, 5 PDF Downloads

I It is difficult for the Traditional statistical Remote sensing classification algorithm to get higher Classifica tion accuracy under the condition of complex state. To solve this problem, BS-GEP algorithm is introduced to the study of remote Sensing image classification Problemsin this paper, to Avoid local converge NCE of the algorithm caused by the population diversity, the characteristic o f the traditional GEP, and solve the problem of getting higher classification Accuracy difficultly under the complex condition state. The experimental results have shown that classification rules based on the BS-GEP classifier can is converted into Mathema Tical expressions and obtain higher classification accuracy. Compared with GEP algorithm, the confused degreeof theclassification results are ivelyLow,and compared with maximum likelihood algorithm, the classification results are relatively clear. The classification accuracy of the classifier has been reached to.

Open Access

Article

Article ID: 327

High resolution Remote Sensing Image retrieval Based on Multi-visual Feature and K-centroid Clustering

by yanfei Peng yanfei Peng

Remote Sensing, Vol.1, No.1, 2024; 165 Views, 6 PDF Downloads

I At present , Resolution remote Sensing image retrieval based on single content has the problem of one-sided description and imprecise information. The color, shape and Texture features of remote sensing images were fully used and combined to form multi-vision remote sensing image retrieval in order to solve this problem. Through a series of iterative operations, the best proportionality coefficient for this three features to treat Types of remote sensing images can be obtained, which gets a better search result. Aiming at the problem of the retrieval speed are slow when searching the large image databasefor the color <b1 6>, shape andTexture features of the remote sensing image respectively , the improved k-centroid clustering algorithm which firstly clustered the images in the remote Sens ing image database is introduced to reduce the retrieval scope as as the improve the retrieval speed. The experimental results show that this method has the retrieval results.