An Open Source Desktop Application for Classification of Remote Sensing Data

被引:0
作者
Garea, Alberto S. [1 ]
Heras, Dora B. [1 ]
Arguello, Francisco [2 ]
机构
[1] Univ Santiago Compostela, CITIUS, Santiago De Compostela, Spain
[2] Univ Santiago Compostela, Elect & Comp Dept, Santiago De Compostela, Spain
来源
2015 IEEE 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS), VOLS 1-2 | 2015年
关键词
remote sensing; spatial-spectral classification; segmentation; desktop application; hyperspectral data;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a desktop application for the analysis and classification of multi and hyperspectral remote sensing images through a simple and intuitive interface. The user can load the data input in raw or MATLAB formats and then a detailed inspection of the different areas or bands of the image can be performed as well as a comparison of pixels based on their spectra. Regarding the classification ability, different spatial-spectral classification schemes have been implemented by using a pipeline consisting of five configurable stages. The modular implementation makes it easy to introduce in the future new classification schemes, new stages to the existing ones, and new algorithms for each stage. It also allows the easy comparison among different classification schemes based on numerical and graphical results. The application is license-free, runs on the Linux operating system and has been developed in C language using the GTK library to build the graphical user interfaces, as well as other free libraries.
引用
收藏
页码:316 / 321
页数:6
相关论文
共 20 条
[1]  
[Anonymous], 2014, MULTISPEC SOFTWARE D
[2]  
[Anonymous], 1999, REMOTE SENSING DIGIT
[3]  
Ayerdi B., 2014, NEUROCOMPUTING
[4]  
Bemabe S., 2013, COMPUT GEOSCI, V6, P1934
[5]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[6]   A morphological gradient approach to color edge detection [J].
Evans, Adrian N. ;
Liu, Xin U. .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2006, 15 (06) :1454-1463
[7]  
Fulkerson B., 2012, Trends and Topics in Computer Vision, Lecture Notes in Computer Science, V6554, P350
[8]  
G. I. C. (UPV/EHU), 2014, HYP REM SENS SCEN
[9]   Exploring ELM-based spatial-spectral classification of hyperspectral images [J].
Heras, Dora B. ;
Argueello, Francisco ;
Quesada-Barriuso, Pablo .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2014, 35 (02) :401-423
[10]   Extreme learning machines: a survey [J].
Huang, Guang-Bin ;
Wang, Dian Hui ;
Lan, Yuan .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2011, 2 (02) :107-122