This study aims to determine the best optimization function for a feedforward neural network using fully polarimetric Quad-Pol data from the ALOS-2 (L-Band) satellite. Data from the Mumbai Region, India, is used to evaluate the performance of different optimization functions. Twelve different functions are considered, including Levenberg-Marquardt, Bayesian Regularization, BFGS Quasi-Newton, Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Fletcher-Powell Conjugate Gradient, Polak-Ribiére Conjugate Gradient, One Step Secant, Variable Learning Rate Gradient Descent, Gradient Descent with Momentum, and Gradient Descent. The ALOS-2 satellite image is filtered using the Lee Sigma Filter and decomposed into seven components using the Gulab 7 Component decomposition (7SD) technique. Neural Network classifiers with 12 different optimization functions are applied on the decomposed image using 7SD. The trained models are analyzed using scores such as individual class accuracies, Kappa Score, Micro & Macro F1 Score, Micro & Macro Precision, Micro & Macro Recall, and Overall Accuracy. The time required to train the different neural networks is also noted. It is observed that Levenberg-Marquardt performs the best as compared to others

Mangroves are vital coastal ecosystems that provide numerous ecological and socio-economic benefits. Accurate detection and monitoring of mangroves are crucial for effective conservation and management. This work aims to determine the best optimization function for a feed-forward neural network that can be used for Mangrove detection. Fully polarimetric Quad-Pol data from the ALOS-2 (L-Band) satellite is used to train the Neural Network. PolSAR data of the Mumbai Region, India is used since it has a huge Mangrove cover. Firstly, the PolSAR image is filtered using the most used Lee Sigma Filter. The filtered image is then decomposed using the Gulab seven component decomposition (7SD) technique. These seven components which act as the feature vectors are used to train the Neural Network classifier. While training 12 different optimization functions are used. The trained models are analyzed using the different evaluation metrics as well as the time required to train the neural networks. It is observed that the Neural Network optimized with Bayesian Regularization and Levenberg-Marquardt optimization functions, gives the highest accuracy.

"Food for U" (UFF) is a revolutionary food-based mobile application designed to cater to the diverse tastes and preferences of food enthusiasts. This app utilizes advanced machine learning and AI algorithms to match users with restaurants that align perfectly with their unique taste buds. A brand inspired by foodies and travellers with an undying love for new experiences. We are working hard to help travellers quit adjusting food during travel and will be introducing food tourism in the world. The app will help fellow travel buddies to enjoy the beautiful scenery of nature while having the most beautiful gift given by nature itself that is food. It will not only help tourist enjoy favorite cuisine, but also, he or she can share his food journey with his or her loved ones.

In recent times, Polarimetric Synthetic Aperture Radar (PolSAR) data is available free of cost due to missions like UAVSAR and Sentinel. As ample data is available the applications are infinite. Till today many researchers have developed various techniques to classify PolSAR data efficiently. They have proposed classification techniques for which the ground truth should be available to train the classifier and validate the results for a particular geographic area. Training the classifier for each area is a time-consuming task and hence there is a need to develop a generalized model which can classify any geographical area acquired from a specific sensor for various land-cover features like water, settlement, forest, wetlands etc. In this paper a generalized machine learning model is proposed which can classify the data acquired from ALOS-PALSAR-2 L-band, irrespective of geographical area for the same land cover features. ANN classifier is used in this work. The classifier is trained using Mumbai and tested for San Francisco and Delhi data. It is observed that the classification accuracy for San Francisco as well as New Delhi is high.

Urban area classification is one of the important applications of Polarimetric Synthetic Aperture Radar (POLSAR). This paper suggests an effective technique for classifying the terrain using different polarimetric decompositions and circular correlation coefficient (CCC) along with the total power. It is observed that after applying decomposition, the oriented urban area is getting classified as forest. The result of proposed technique shows that the classification accuracy increases significantly for settlement class i.e., most of the oriented settlement gets classified as settlement not as forest.

Unmanned Aerial Vehicle (UAV) based aerial mapping has taken over the surveying industry thanks to low costs and ease of use. Although these UAVs have relatively high-resolution imaging systems, there exists a near exponential relationship between the ground sampling distance (GSD) and the number of images required - which is a function of flight altitude. To tackle this, we use a generative network based super-resolution approach to increase the GSD of images which effectively reduces flight time. In this paper we test the efficiency and efficacy of this approach using two multimedia super-resolution implementations. We also provide quantitative results comparing the two using various image processing metrics.

Due to unprecedented growth in freely and easily available remote sensing data, it is being used in plenty of applications and creating opportunities to contribute in this field. At the same time at the educational perspectives, very few countries are paying attention to remote sensing education at wider scale. Many of the developing countries still do not have exposure to remote sensing education. Therefore, it is required to have a learning platform to reach out to the learners using simple and easily available technologies. We are proposing mobile based remote sensing learning platform which will focus understanding, training, and research in remote sensing domain and have maximum outreach to all interested learners.

Future PolSAR missions are expected to collect vast quantities of data, which can significantly add to the storage cost of various geospatial cloud driven applications. Data compression techniques like those prescribed by the JPEG2000 (JP2) standard might help counteract this cost. However, it is important to measure the impact on target application performance due to these techniques. In this paper, the impact of JP2 and JPEG compression on classification performance of PolSAR data is studied and it has been found that compression has no significant impact on Deep Neural Network (DNN) classification performance.

Land-cover classification is one of the core applications in the field of remote sensing. It is a valuable resource for city planners to achieve sustainable development. Many metropolitan cities are experiencing disorganized growth with a high intensity of urban sprawl due to the economic pull and better standards of living offered in metropolitan cities when compared to the surrounding rural areas. If this pattern of growth continues, it will lead to unsustainable development. This leads to an increase in pressure on urban infrastructure and the ecosystem. The traditional methods which are used for urban mapping are time consuming. Instead, Microwave Remote Sensing can be used to acquire geographical data which can be used to develop a decision support system to help urban settlement planners. This paper suggests the use of Semantic Segmentation to extract the various land cover features from Polarimetric Synthetic Aperture Radar (PolSAR) images using the Fully Convolutional Network (FCN) based modified UNet architecture, that will help in the analysis of land-cover in areas prone to urban sprawl by utilizing the elements of coherency matrix.

RISAT-1 data is acquired over Mumbai in hybrid and linear dual polarizations. The mean and standard deviation of backscattering coefficients ( σ0) are computed and have been analyzed for various land features. Classification accuracy between RISAT-1 hybrid and dual polarimetric data has been compared. The effect of different multilook on the classification accuracy is also studied. Wishart supervised and Support Vector Machine (SVM) classifiers are used for this study. It has been observed that the classification accuracy can be improved by using m-δ or m-χ decomposition along with Circular Polarization Ratio (CPR) and SPAN of hybrid polarimetric data.

Geographic Information System (GIS) and Remote Sensing (RS) are domains that are recognized globally with lot of on-going research world-wide. In India, the scenario is just the opposite. These domains are less widespread in the field of higher education. This paper focuses on the issues that are prevalent in GIS and RS education in India. Literature survey states that out of 789 Universities, 11,443 Stand-alone institutions and 658 Autonomous Colleges only 40 institutions offer GIS and RS courses. An online survey was conducted to study the issues faced by the instructors and learners. The study of the responses endorses the fact that in India it is required to create awareness of these courses by including them in the University curriculum and providing necessary training.

The target decomposition techniques give more information about scattering mechanism than obtained through covariance or coherency matrix. In this paper, the effect of various parameters of Touzi decomposition on classification accuracy is studied. The work shows that out of many Touzi parameters, the first components of α, Φ, λ, τ along with span can effectively classify various land features. The influence of helicity is more prevalent at L-band compared to C-band. The effect of different non-parametric classifiers on classification accuracy is also studied.

The launch of RISAT-1 Indian remote sensing satellite on 26thApril 2012, made it possible to collect hybrid polarimetric data from a space-borne sensor. The RISAT-1 C-band compact polarimetry data acquired over Mumbai is analyzed and assessed for classification of various land features and also compared with other fully polarimetric spaceborene SAR data sets. For better comparison, RISAT-1 C-band and RADARSAT-2 C-band simulated compact polarimetric data is classified and compared.

Polarimetric Synthetic Aperture Radar (PolSAR) data is available at different frequencies and polarizations from various sensors like ALOS-PALSAR, Envisat ASAR, TerraSAR-X. This study compares the classification accuracies obtained with fully polarimetric and dual-polarization L-band ALOS-PALSAR data over Mumbai and Sundarban area. We have also compared dual polarized ALOS-PALSAR L-band TerraSAR-X X-band and Envisat C-band SAR data acquired over Mumbai area. IRS-P6 optical data over the same area has been used to compare the classification accuracy between optical and SAR data. The change in accuracy due to the phase information of SAR data is also assessed by comparing the classified results of intensity and complex images for all the possible polarization combinations such as (HH, HV), (HH, W) and (HV, VV) at L-band. The results show that the fully polarimetric mode gives maximum classification accuracy (above 95%). It is observed that among dual polarized data, complex (HH, VV) combination gives the maximum (above 92%) accuracy.

Polarimetric SAR data from ALOS PALSAR, SIR-C and ENVISAT ASAR over wet and arid regions were processed for classification and soil moisture estimation. HH and HV dual polarized ALOS PALSAR could classify wetlands of Mumbai coastal area with an accuracy of 96%, whereas fully polarized SIR-C data over Kolkata gave 92% accuracy. The accuracies are based on selected training areas and not based on test areas. ALOS PALSAR could clearly discriminate water, mangrove forest and ocean water. With Dual polarized data, discrimination between Ocean water and wetlands is not possible. Several features in arid data can also be classified using PALSAR data in addition to the estimation of soil moisture.