Analysis of Image Enhancement Techniques Used in Remote Sensing Satellite Imagery

Bajpai, Kriti; Soni, Rishi

doi:10.5120/ijca2017914884

Cited by 14 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fourier transforms are used to transform the image into the domain of spatial frequency; and the third category is color enhancement. Color enhancement techniques transform the grey-level image into color image models, such as RGB, HIS and CMY, and for remote sensing images, it is mainly the HIS Transform and Pseudo-Color Transform [3][4][5][6]. This study focuses on the spatial domain method, which is more concerned with point-to-point processing of remote sensing images, such as common NDVI, NDBI, linear stretching, PCA, TCT, etc.…”

Section: Of 22mentioning

confidence: 99%

A Novel Remote Sensing Image Enhancement Method, the Pseudo-Tasseled Cap Transformation: Taking Buildings and Roads in GF-2 as an Example

et al. 2023

View full text Add to dashboard Cite

With the improvements in sensor accuracy, the spectral features of high-resolution remote sensing images become more complex. As a result, the classification accuracy for land cover classification decreases. Remote sensing image enhancements can improve the visual effect and the intra-class consistency and enhance the characteristics of ground objects. These enhancements are important for both image interpretation and improving image segmentation accuracy. In this study, we propose a pseudo-tasseled cap transformation (pseudo-TCT) through an orthogonal linear transformation of Gaofen-2 (GF-2) images using the untransposed tasseled cap transformation (TCT) coefficients, and further, enhance the visual effect and the separability among ground objects by linear stretching and percentage truncation stretching. To examine the separability among ground objects in the pseudo-TCT image, we used K-Means clustering, ISODATA clustering and 3D visualization of the spectral features of typical ground objects. The results show that the separability of buildings and roads from background objects is better than in the original image and the TCT image, and typical ground objects are effectively distinguished. Additionally, we visualized intra-class consistency by calculating the mean Euclidean distance between the pixel values of each point and the pixel values of its eight neighboring points and calculated the standard deviation of the intra-class consistency images. The results indicate that the secondary textures of the objects were weakened, and edges were made clearer, enhancing intra-class consistency. The pseudo-TCT is effective, at least in our work, and could be a candidate for image enhancement under certain applications.

show abstract

Section: Of 22mentioning

confidence: 99%

A Novel Remote Sensing Image Enhancement Method, the Pseudo-Tasseled Cap Transformation: Taking Buildings and Roads in GF-2 as an Example

et al. 2023

View full text Add to dashboard Cite

show abstract

“…And it could differ from numerous sensors and satellites. And with photographs that have been received and have had various noise removal algorithms done to improve the image quality [2] [3]. By using different enhancement algorithms to create a sharper and more contrasted brightness improvement procedure from noise components, these photographs primarily have quality concerns.…”

Section: A Image Enhancementmentioning

confidence: 99%

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

Kumar,

Uma

et al. 2023

IJRITCC

View full text Add to dashboard Cite

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%.

show abstract

“…The resultant outcome work as a utility for accurate analysis and not for supplementary spectral analysis [12] …”

Section: Image Fusionmentioning

confidence: 99%

“…In the direction of examining an enormous image, they have used ENVI 5.0 software [12]. It provides the GUI platform to generate the panchromatic data and clip the data to make our own data set from imagery, because the original image is difficult to read in MATLAB 9.2…”

Section: Preprocessingmentioning

confidence: 99%

Remotely Sensed Image Segmentation using Multiphase Level-Set ACM

Bajpai¹,

Soni²

2017

IJCA

Self Cite

View full text Add to dashboard Cite

In remote sensing image analysis, segmentation of an image is an important aspect. It classifies similar pixels within the image. Image Segmentation is helpful in analyzing the patterns, objects, and edges within an image. There are many ways for performing image segmentation. In this paper, we are segmenting a satellite image using Multiphase Chan-Vese model. Chan-Vese models are based on 'Active Contours without edges'. Active contour model is also known as Snake and Energy-Based Model, which is finding local minima in the equivalent energy function. ChanVese model gives effective results of segmented image. The multiphase level set construction is mechanized to avoid the drawback of overlap and vacuum; it can also signify edges with convoluted topologies. Researchers conclude in this paper with the findings that the multiphase CV method can give a sensible segmented image of satellite imagery with 2D-DWT, when they manipulate Heaviside function.

show abstract

Analysis of Image Enhancement Techniques Used in Remote Sensing Satellite Imagery

Cited by 14 publications

References 4 publications

A Novel Remote Sensing Image Enhancement Method, the Pseudo-Tasseled Cap Transformation: Taking Buildings and Roads in GF-2 as an Example

A Novel Remote Sensing Image Enhancement Method, the Pseudo-Tasseled Cap Transformation: Taking Buildings and Roads in GF-2 as an Example

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

Remotely Sensed Image Segmentation using Multiphase Level-Set ACM

Contact Info

Product

Resources

About