International Journal of Remote Sensing Applications最新文献_第6页

Geomorphometric Analysis for Estimation of Sediment Production Rate and Run-off in Tuirini Watershed, Mizoram, India 印度米佐拉姆邦图里尼流域产沙速率和径流估算的地貌分析

International Journal of Remote Sensing Applications Pub Date : 1900-01-01 DOI: 10.14355/IJRSA.2015.05.008

F. Ahmed, K. S. Rao

{"title":"Geomorphometric Analysis for Estimation of Sediment Production Rate and Run-off in Tuirini Watershed, Mizoram, India","authors":"F. Ahmed, K. S. Rao","doi":"10.14355/IJRSA.2015.05.008","DOIUrl":"https://doi.org/10.14355/IJRSA.2015.05.008","url":null,"abstract":"In hilly areas like Mizoram, sediment production rate and run-off estimation for land parcels is of utmost importance to the efforts of soil and water conservation work. The study demonstrated the use of remotely sensed data in conjugation with GIS for the sustainable development of watershed. The terrain is prone to erosion due to steeper slopes associated with high relief and drainage density. The different geomorphometric parameters of the study area have been computed with the aid of ArcGIS-10.2 software. SPR and run-off rate of the watershed was estimated on the basis of morphometric parameters. The Tuirini watershed is designated as 6th order stream comprises an area of about 420 sq.km. The mean bifurcation ratio indicates strong structural control over the drainage development. The values of drainage density and texture ratio indicate that the area is composed of impermeable rocks associated with very fine drainage texture. The analysis of shape and relief parameters shows that watershed is having elongated shape and structurally complex with high relief. The estimated value of SPR and runoff rate suggests that the watershed produces moderate amount of sediments annually with high discharge of runoff due to high relief with steeper slopes.","PeriodicalId":219241,"journal":{"name":"International Journal of Remote Sensing Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132864319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Optimizing Activation Function in Deep Artificial Neural Networks Approach for Landcover Fuzzy Pixel-Based Classification 基于深度人工神经网络的土地覆盖模糊分类激活函数优化

International Journal of Remote Sensing Applications Pub Date : 1900-01-01 DOI: 10.14355/IJRSA.2017.07.001

A. Serwa

引用次数: 6

ML-based Approaches for Joint SAR Imaging and Phase Error Correction 基于ml的联合SAR成像与相位误差校正方法

International Journal of Remote Sensing Applications Pub Date : 1900-01-01 DOI: 10.14355/IJRSA.2016.06.002

H. Abeida

{"title":"ML-based Approaches for Joint SAR Imaging and Phase Error Correction","authors":"H. Abeida","doi":"10.14355/IJRSA.2016.06.002","DOIUrl":"https://doi.org/10.14355/IJRSA.2016.06.002","url":null,"abstract":"This paper addresses a series of iterative sparse recovery approaches with application to the synthetic aperture radar (SAR) imaging which suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. The proposed phase-error correction approaches combine the maximum a posterior (MAP) algorithm and the iterative sparse maximum likelihood-based (SMLA) approaches (referred to as the PE-MAP-SMLA approaches) to solve a joint optimization problem to achieve phase errors estimation and SAR image formation simultaneously. A new PESLIM approach is also proposed that extends the idea of the classical sparse and learning via iterative minimization (SLIM) approach. A closed-form expression for the recursive estimate of the phase errors parameters is derived. A general form of each of these iterative approaches consists of three steps, the first of which is for image formation, the second is for phase errors estimation and the last is for nuisance parameters estimation. The proposed approaches can correct the phase errors accurately, and the reconstruction quality of the SAR images can be improved significantly. Finally, simulation results of 1-D spectral estimation and 2-D SAR imaging examples are generated to show the effectiveness of the proposed approaches.","PeriodicalId":219241,"journal":{"name":"International Journal of Remote Sensing Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122471650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Land Features Extraction from Landsat TM Image Using Decision Tree Method 基于决策树方法的Landsat TM影像地物提取

International Journal of Remote Sensing Applications Pub Date : 1900-01-01 DOI: 10.14355/IJRSA.2016.06.011

Jason Yang, Feihong Wang

引用次数: 2