Land Cover/Land use Mapping Using Different Wavelet Packet Transforms for LISS IV Madurai Imagery

Rajesh, S.; Arivazhagan, S.; Moses, K. Pratheep; Abisekaraj, R.

doi:10.1007/s12524-011-0154-7

Cited by 16 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test and verify the proposed algorithm, different datasets from IRS-1A, and SPOT remote sensing images, including medium and high-resolution images, are used as one set. The IRS-1A image is obtained from the Indian Remote Sensing Satellite [22]. The configuration of IRS-1A image located at Calcutta shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

View full text Add to dashboard Cite

Now-a-days, sensing of remote satellite data processing is a very challenging task. The current development of satellite technology has led to explosive growth in quantity as well as the quality of the High-Resolution Remote Sensing (HRRS) images. These images can sometimes be in Gigabytes and Terabytes, which is heavy to load into the memory and also takes more time for processing. To address the challenges of processing HRRS images, a distributed map Reduce framework is proposed in this paper. This paper reflects Map-reduce as a distributed model using the Hadoop framework for processing large amounts of images. To process large amounts of images, block-based and size-based methods are introduced for effective processing. From the experiments, the proposed framework has proven to be effective in performance and speed.

show abstract

Section: Resultsmentioning

confidence: 99%

Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…The work in [1] proposed a system in which statistical and co-occurrence features of the input patterns are first extracted, and those features are used for classification [11-13, 20, 38, 48]. The continuous wavelet transform of a 1-D signal f(x) is defined using Eq.…”

Section: Extraction Of Features Using Wavelet Packet Transformmentioning

confidence: 99%

Land Cover/Land Use Mapping Using Soft Computing Techniques with Optimized Features

Rajesh¹,

Nisia²

2020

Land Use Change and Sustainability

View full text Add to dashboard Cite

The chapter discusses soft computing techniques for solving complex computational tasks. It highlights some of the soft computing techniques like fuzzy logic, genetic algorithm, artificial neural network, and machine learning. The classification of the remotely sensed images is always a tedious task. So, here we explain how these soft computing techniques could be used for image classification. Image classification mainly concentrates on the feature's extraction process. The features extracted in an efficient manner improve classification accuracy. Hence, the different kinds of features and different methods for these extractions are explained. The best extracted features are selected using genetic algorithm. Various algorithms are shown and comparisons are made. Finally, the results are verified using a hypothetical case study.

show abstract

“…Many techniques have been introduced to retrieve LULC image from Landsat-8 OLI data to investigate better classification accuracy (Phiri et al, 2017;Jia et al, 2014;Taufik et al, 2016). Several DWT based methods have been developed for LULC classification of many satellites (Chen et al, 2007;Rajesh et al, 2011;Myint et al, 2006). In the present study, DWT approach has been used on Landsat -8 OLI data for LULC classification.…”

Section: Introductionmentioning

confidence: 97%

Land Use and Land Cover Classification of Multispectral Landsat-8 Satellite Imagery Using Discrete Wavelet Transform

Sharma

Prasad

Mishra

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Land use and land cover (LULC) classification of satellite imagery is an important research area and studied exclusively in remote sensing. However, accurate and appropriate land use/cover detection is still a challenge. This paper presents a wavelet transform based LULC classification using Landsat 8-OLI data. The study area for the present work is a small part of Varanasi district, Uttar Pradesh, India. The atmospheric correction of the image was performed using Quick Atmospheric Correction (QUAC) method. The image was decomposed into its approximation and detail coefficients up to eight levels using discrete wavelet transform (DWT) method. The approximation images were layer stacked with the original image. The minimum distance classifier was used for classifying the image into six LULC classes namely water, agriculture, urban, fallow land, sand, and vegetation. The classification accuracy for all decomposition levels was compared with that of classified product based on original multispectral image. The classification accuracy for multi-spectral image was found to be 75.27%. Whereas, the classification accuracies were found to improve up to 85.97%, 88.87%, 93.47%, 95.03%, 93.01, 92.32% and 90.80% for second, third, fourth, fifth, six, seventh and eight level decomposition, respectively. The significantly improved accuracy was found for images decomposed at level five. Thus, the approach of DWT for LULC classification can be used to increase the classification accuracy significantly.</p>

show abstract

Land Cover/Land use Mapping Using Different Wavelet Packet Transforms for LISS IV Madurai Imagery

Cited by 16 publications

References 23 publications

Distributed Framework for Processing High Resolution Remote Sensing Images

Distributed Framework for Processing High Resolution Remote Sensing Images

Land Cover/Land Use Mapping Using Soft Computing Techniques with Optimized Features

Land Use and Land Cover Classification of Multispectral Landsat-8 Satellite Imagery Using Discrete Wavelet Transform

Contact Info

Product

Resources

About