Browsing by Author "Prajwal, R."

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    A Dual Phase Approach for Addressing Class Imbalance in Land-Use and Land-Cover Mapping From Remotely Sensed Images
    (Institute of Electrical and Electronics Engineers Inc., 2024) Putty, A.; Annappa, B.; Prajwal, R.; Pariserum Perumal, S.P.
    Semantic segmentation of remotely sensed images for land-use and land-cover classes plays a significant role in various ecosystem management applications. State-of-the-art results in assigning land-use and land-cover classes are primarily achieved using fully convolutional encoder-decoder architectures. However, the uneven distribution of the land-use and land-cover classes becomes a major hurdle leading to performance skewness towards majority classes over minority classes. This paper proposes a novel dual-phase training, with the first phase proposing a new undersampling technique using minority class focused class normalization and the second phase that uses this learnt knowledge for ensembling to prevent overfitting and compensate for the loss of information due to undersampling. The proposed method achieved an overall performance gain of up to 2% in MIoU, Kappa, and F1 Score metrics and up to 3% in class-wise F1-score when compared to the baseline models on Wuhan Dense Labeling, Vaihingen and Potsdam datasets. © 2013 IEEE.
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    Semantic Segmentation of Remotely Sensed Images using Multisource Data: An Experimental Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2024) Putty, A.; Annappa, B.; Prajwal, R.; Pariserum Perumal, S.P.
    Remotely sensed data obtained from diverse sensors provide rich information for a wide range of applications in remote sensing, such as land use and land cover mapping. Due to the availability of a large amount of data, advanced deep-learning techniques have been incorporated into this domain. However, these techniques require a significant amount of annotated data, which can be challenging to obtain for land-use and land-cover mapping. Multisource data fusion has become crucial in remotely sensed image analysis to overcome this challenge, providing significant benefits across various applications. This paper analyzes the fusion of multisource data tailored for land-use and land-cover mapping. The analysis showcases that incorporating the novel knowledge transfer approach from multisource data has helped to achieve a 1-6% improvement in mIoU for the Kaggle Aerial Image dataset. © 2024 IEEE.