Abstract: Embodiments herein provide a method and system for a hyperspectral artificial vision for machines. The system receives a hyperspectral signal of a target material as an input to a neural network model. The system initializes by selecting the number of primitive layers to be used. The system iteratively cycles through all training data (pixels) and updating weights for each unsuccessful material class prediction. Model with two primitives serves as baseline, after which the system adds another primitive layer and repeats the training procedure. The system keeps repeating these processes until obtains convergence. Where the system come to a halt, the system obtains the optimal number of primitives for the given materials. The generated new color pixel is used as a discriminator to aid in locating the target material. The new artificial color is a mixture of weighted chromatic primitives which are optimized for sensitivity/(Spectral Response Functions) SRFs.

Abstract: Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral/multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.

Abstract: Assessing sustainability performance of an enterprise is a challenging task. Embodiments of present disclosure provide a method and system for SDG performance assessment of an enterprise with a conceptually simpler data model and processing pipeline. Enterprise data collected from hard and soft sensors is mapped to appropriate indicators of the SDGs. Further, a semantic network is constructed with nodes corresponding to each indicator and edges connecting nodes belonging to same SDG. Each node of the semantic network is further linked to a first layer of a neuro fuzzy network which calculates degree of impact of the indicator on Social, Economic and Environment values. Output of the first layer activates second layer of the neuro fuzzy network which determines BBV scores indicating whether the indicator is a burden, benefit, or vulnerability. The BBV scores are transformed to a colour space to generate a colour that indicates SDG performance of the enterprise.

Abstract: The embodiments of present disclosure herein address problem of urban metabolism with respect to water demand and carbon dioxide emissions, the discussion is based on the reported data by the urban areas. The embodiments herein provide a method and system for estimating urban metabolism based on remotely sensed data. The system is configured to develop a model for identifying correct features from image or proxy features from image and then develop/use relation between the image feature or proxy feature from the image with the urban metabolic parameter. Further, the system develops an urban growth model which predicts spatial extent of the given proxy features. The urban growth scenario for each such conditions is different. By changing conditions of the model, different growth scenarios are played out. For each scenario, at least one urban metabolic parameter is predicted by taking output of the urban growth predictor.

Abstract: This disclosure provides a method and system for spectrum matching for hyperspectral and multispectral data. Conventional methods using geometric or statistical distance measures for spectral matching considers two spectra having equal length or having large amplitude difference. These methods do not consider amplitude difference in the spectra or spectra with unequal lengths. Embodiments of the present disclosure is formulated as a measurement of transformation required for converting a target spectrum to a reference spectrum or vice versa. The method computes a transformation cost between the two spectra for spectral matching. The transformation cost is globally optimized to obtain an optimal transformation cost which represents the optimal spectrum matching of the target spectrum with the reference spectrum.

Abstract: The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.

Abstract: State of art techniques performing image labeling of remotely sensed data are computation intensive, consume time and resources. A method and system for efficient retrieval of a target in an image in a collection of remotely sensed data is disclosed. Image scanning is performed efficiently, wherein only a small percentage of pixels from the entire image are scanned to identify the target. One or more samples are intelligently identified based on sample selection criteria and are scanned for detecting presence of the target based on cumulative evidence score Plurality of sampling approaches comprising active sampling, distributed sampling and hybrid sampling are disclosed that either detect and localize the target or perform image labeling indicating only presence of the target.

Abstract: Hyperspectral data associated with hyperspectral images received for any Region of Interest (ROI) is in form of number of pixel vectors. Unlike conventional methods in the art that treat this pixel vector as a time series, the embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Learning from pixel vectors directly may not capture the spectral details efficiently. The intuition is to learn the spectral features as represented by the shape of a spectrum or in other words the features which a spectroscopy expert uses to interpret the spectrum. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels.

Abstract: This disclosure relates generally to image processing, and more particularly to method and system for image reconstruction using deep dictionary learning (DDL). The system collects the degraded image as test image and processes the test image to extract sparse features from the test image, at different levels, using dictionaries. The extracted sparse features and data from the dictionaries are used by the system to reconstruct the HR image corresponding to the test image.

Abstract: The present disclosure provides a method and a system for optimizing Hidden Markov Model based land change prediction. Firstly, remotely sensed data is pre-processed and classified into a plurality of land use land cover classes (LULC). Then socio-economic driver variables data for a pre-defined interval of time are provided from a database. A Hidden Markov Model (HMM) is defined with LULC as hidden states and socio-economic driver variables data as observations and trained for generating a MINI state transition probability matrix. Again the defined MINI is trained by taking input data from scenario based temporal variables to generate another set of HMM state transition probability matrix. The generated MINI state transition probability matrix is then integrated with a spatio-temporal model to obtain an integrated model for predicting LULC changes to generate at least one prediction image.

Abstract: A system and method for identifying real time change in a scene of an unknown environment using an unmanned vehicle. In the context of unmanned vehicle navigation, it is critical to calculate the saliency map in real time and employ them in scene understanding. This will reduce the search space and ensure that the process is quicker. A domain specific ontology is created and a saliency model is developed. The saliency model detects key domain specific regions of interest in two consecutive images. The regions of interest is used for registration and change detection. The system is detecting the change by using visual saliency as an abstract feature that is developed in the environment. Probability of change is derived using the salient maps of the two images.

Abstract: System and method of the present disclosure provide a linguistic approach to image processing. Prior art focused on extracting well-defined single objects occupying large portion of an image area. However, there was no focus on higher level semantics or distribution of object categories within the image. In contrast to imagery by handheld devices, remotely sensed data contains numerous objects because of relative large coverage and distribution over objects is critical to analyzing such large coverage. Accordingly, in the present disclosure, a generative statistical model is defined wherein an aerial image is modelled as a collection of the one or more themes and each of the one or more themes is modelled as a collection of object categories. The model automatically adapts to a scale of the aerial image and appropriately identifies the themes which may be used for applications including monitoring land use, infrastructure management and the like.

Abstract: This disclosure relates generally to image processing, and more particularly to method and system for image reconstruction using deep dictionary learning (DDL). The system collects the degraded image as test image and processes the test image to extract sparse features from the test image, at different levels, using dictionaries. The extracted sparse features and data from the dictionaries are used by the system to reconstruct the HR image corresponding to the test image.

Abstract: Disclosed is a method and system for creating labels for cluster in computing environment. The system comprises receiving module, candidate items selector, combination array generator, coverage value analyzer, candidate pair selector, unique word filter and cluster label selector. Receiving module receives input data and candidate items selector selects candidate items occurring repetitively using n-gram technique to generate list of candidate items with frequency of occurrence. Combination array generator selects candidate items to populate two-dimensional array wherein each array element represents pair of n-gram. Coverage value analyzer determines coverage value for each pair of n-gram from array. Candidate pair selector selects pairs of n-gram from two-dimensional array to process and generate list of candidate pairs. The unique word filter determines number of unique words in each candidate pair.

Abstract: System and method of the present disclosure provide a linguistic approach to image processing. Prior art focused on extracting well-defined single objects occupying large portion of an image area. However, there was no focus on higher level semantics or distribution of object categories within the image. In contrast to imagery by handheld devices, remotely sensed data contains numerous objects because of relative large coverage and distribution over objects is critical to analyzing such large coverage. Accordingly, in the present disclosure, a generative statistical model is defined wherein an aerial image is modelled as a collection of the one or more themes and each of the one or more themes is modelled as a collection of object categories. The model automatically adapts to a scale of the aerial image and appropriately identifies the themes which may be used for applications including monitoring land use, infrastructure management and the like.

Abstract: A system and method for identifying real time change in a scene of an unknown environment using an unmanned vehicle. In the context of unmanned vehicle navigation, it is critical to calculate the saliency map in real time and employ them in scene understanding. This will reduce the search space and ensure that the process is quicker. A domain specific ontology is created and a saliency model is developed. The saliency model detects key domain specific regions of interest in two consecutive images. The regions of interest is used for registration and change detection. The system is detecting the change by using visual saliency as an abstract feature that is developed in the environment. Probability of change is derived using the salient maps of the two images.

Abstract: The present disclosure provides a method and a system for optimizing Hidden Markov Model based land change prediction. Firstly, remotely sensed data is pre-processed and classified into a plurality of land use land cover classes (LULC). Then socio-economic driver variables data for a pre-defined interval of time are provided from a database. A Hidden Markov Model (HMM) is defined with LULC as hidden states and socio-economic driver variables data as observations and trained for generating a HMM state transition probability matrix. Again the defined HMM is trained by taking input data from scenario based temporal variables to generate another set of HMM state transition probability matrix. The generated HMM state transition probability matrix is then integrated with a spatio-temporal model to obtain an integrated model for predicting LULC changes to generate at least one prediction image.

Abstract: Disclosed is a method and system for creating labels for cluster in computing environment. The system comprises receiving module, candidate items selector, combination array generator, coverage value analyzer, candidate pair selector, unique word filter and cluster label selector. Receiving module receives input data and candidate items selector selects candidate items occurring repetitively using n-gram technique to generate list of candidate items with frequency of occurrence. Combination array generator selects candidate items to populate two-dimensional array wherein each array element represents pair of n-gram. Coverage value analyzer determines coverage value for each pair of n-gram from array. Candidate pair selector selects pairs of n-gram from two-dimensional array to process and generate list of candidate pairs. The unique word filter determines number of unique words in each candidate pair.