Multispectral Image Processing System Developed in CARTOMORPH Software—NDVI Module

Tiago Oyan Aguiar,Guilherme Pina Cardim,Samara Calçado De Azevedo,Erivaldo Antonio Da Silva,Miriam Maria Pedrosa

doi:10.4236/ars.2018.72007

Abstract

In this paper, we have evaluated the multispectral module implemented in the CARTOMORPH software, which is a public domain software under development by the mathematical morphology research group at FCT/UNESP. The aim of CARTOMORPH is to provide an open software with feature extraction methods and a library that contains a variety of implemented code (functions) that can be easily operated by users through graphical interface. The multispectral image processing system has been developed to allow feature discrimination by operations between bands from Remote Sensing images. The normalized difference vegetation index (NDVI) was selected to be implemented on CARTOMORPH because of its acknowledged performance for monitoring of vegetation and cartographic applications. The experiments were applied to multispectral bands from Quick Bird image and the results were compared with those provided by the SPRING and ENVI software. Former is a Brazilian free software, developed by National Institute for Space Research-INPE and dedicated to image processing and Geographic Information Systems (GIS) analysis, as well as ENVI, which is a traditional remote sensing image analysis system. The results prove that the implementation of this module is correct, allowing potential usage in the field of cartography and for environmental applications.

Highlights

Results obtained from normalized difference vegetation index (NDVI) is known to range between ±1, where generally the higher pixels values are correlated with vegetation cover, density and vigour
The SPRING and ENVI software was used for comparison, as mentioned earlier, and the results found with NDVI were visually identical
The evaluation of results achieved with the cmNDVI algorithm available in the CARTOMORPH software indicated a successful implementation

Summary

Introduction

The society’s demand for up-to-date surface information has been steadily in-. One of the possible solutions employed to solve this ongoing problem has been data integration from remote sensing (RS) with digital image processing (DIP) techniques [1]. RS imagery are timely and cost-effective data which plays a significant role in a wide range of real-world applications, such as natural disasters assessment [2], vegetation monitoring [3] and climate change understanding [4]. The developments of sensors and imagery have required improvements in computer software and hardware to handle with data complexity. Besides the increased availability of spatial details, new challenges were imposed to the traditional RS methods which are a core research topic to be pursued [5]

Methods

Results

Conclusion