Akanksha Garg

Wavelet based resolution enhancement for low resolution satellite images

Satellite images play a major role in the analysis of land cover, topographic analysis, geosciences etc. There has always existed a tradeoff between the image resolution and the image cost. In this paper, a modified discrete wavelet transform and interpolation based technique is proposed for enhancing the resolution of satellite images having low resolution in such a way that a highly resolved satellite image can be obtained without losing any image information. The advent of DWT has given a major impetus to many techniques based on achieving super resolution starting with a single low resolution image. In the proposed method, DWT is employed on the input satellite image to decompose it into sub-bands then the high frequency sub-bands and the input low resolution satellite image have been interpolated to obtain four interpolated images which are later combined after minor alterations to the interpolated input image using IDWT. The quantitative peak signal-to-noise ratio (PSNR) and classification results show that the resolution has been enhanced to a good scale without losing any information content present in the satellite image. The quality assessment parameters also illustrate the supremacy of the proposed technique over the conventional techniques.

Development of an Adaptive Approach for Precision Agriculture Monitoring with Drone and Satellite Data

For better agricultural productivity and food management, there is an urgent need for precision agriculture monitoring at larger scales. In recent years, drones have been employed for precision agriculture monitoring at smaller scales, and for past few decades, satellite data are being used for land cover classification and agriculture monitoring at larger scales. The monitoring of agriculture precisely over a large scale is a challenging task. In this paper, an approach has been proposed for precision agriculture monitoring, i.e., the classification of sparse and dense fields, which is carried out using freely available satellite data (Landsat 8) along with drone data. Repeated usage of drone has to be minimized and hence an adaptive classification approach is developed, which works with image statistics of the selected region. The proposed approach is successfully tested and validated on different spatial and temporal Landsat 8 data.

Critical Analysis of Model-Based Incoherent Polarimetric Decomposition Methods and Investigation of Deorientation Effect

This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power.

A novel approach for optimal weight factor of DT-CWT coefficients for land cover classification using MODIS data

Presently, there is a need to explore the possibility to maximize the use of MODIS (Moderate Resolution Imaging Spectroradiometer) data as it has very good spectral (36 bands) and temporal resolution whereas its spatial resolution is moderate i.e. 250m, 500m, and 1km. Because of its moderate spatial resolution, its application for land cover classification is limited. Therefore, in this paper, an attempt has been made to enhance its spatial resolution and utilize the information contained in the different bands together to achieve good land cover classification accuracy, so that, in future, MODIS data can be used more effectively. For resolution enhancement, modified dual tree complex wavelet transform (DT-CWT) has been employed, where DT-CWT has been modified by critically analyzing the effect of weight factor of the DT-CWT coefficients on land cover classification. For this purpose, image statistics parameter like Mean of the image has also been considered. The proposed technique has been applied on the six bands of MODIS data which have spatial resolution of 500m. It is observed that weight factor of the high-frequency sub-bands is quite sensitive for computation of classification accuracy.

Class wise optimal feature selection for land cover classification using SAR data

Conventional methods for classifying SAR data, such as H-α decomposition, Wishart classifier etc. are quite complex and classifies data only on the basis of polarimetric information. With the advent of distinct feature types, their role in land cover classification using SAR data could be analysed. For the sake of classification, researchers are extracting and combining several features in order to obtain the best attainable accuracy. But the usage of several feature type is not only increasing the computational complexity, but also the salience of each of the feature type remains unhighlighted. Hence, it became difficult to analyse that which feature type are best suitable for classification and selection of suitable features for land cover classification is challenging as each feature has its own significance level. Therefore, in this paper class wise, optimal feature selection for land cover classification has been performed using SAR data. For optimal feature selection, four types of feature set polarimetric features, texture features, color features and wavelet features have been examined. For class wise feature subset selection separability index criteria and classification results obtained using Naive Bayes classifier has been utilized. With the proposed methodology overall 10 features has been selected among the total 37 feature analysed with fine land cover classification accuracy of 91%.

A comparison of wavelet based techniques for resolution enhancement of Moderate Resolution satellite images

Satellite image resolution enhancement is an active area of research. There are many techniques which perform well in the Standard image domain but tend to give inconsistent and unacceptable results in the case of satellite images. Thus there is a need to modify the existing techniques and develop new ones to overcome this short coming. This paper presents several image resolution enhancement techniques like WZP (Wavelet Zero Padding), WZP-CS (Wavelet Zero padding and Cycle Spinning), DWT (Discrete Wavelet Transform) and DWT-SWT with Error back projection that were adapted to the satellite images by modifications to the original technique. These techniques were applied on MODIS (Moderate Resolution Imaging Spectroradiometer) images and the results have been evaluated using standard quality assessment parameters like PSNR, RMSE and Correlation Coefficient. The initial moderate resolution and the final high resolution images obtained through the different techniques are also illustrated in the paper. The outcome of this evaluation indicates that the techniques which achieved very good PSNR values in Standard images do not necessarily reiterate the same in case of satellite images. The results also reinforce that the modified techniques fare well to preserve the information content of the satellite images and improve the quality.