Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Urban land cover classification using hyperspectral data
    (International Society for Photogrammetry and Remote Sensing, 2014) Hegde, G.; Mohammed Ahamed, J.M.; Hebbar, R.; Raj, U.
    Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers viz., Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.
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    Spectral Indices based Land Cover Classification using Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Payani, C.A.; Gupta, C.; Anand Kumar, M.
    In this paper, we use Landsat 8 and 9 satellite data to predict the land area that is suitable for agriculture and farming. Early identification and deriving insights from areas and their land properties will give us the scope for better utilization of the area. To achieve this, We used 2 manually created datasets using google earth engine. Even though the main motive is to predict the productive cropland using the created dataset. classification task we intended is to identify the type of region in the given area of land as Water, Barren land, cropland, forests, and urban areas. Deep feed forward neural network and 1D CNN models are used for this classification. The DFNN model consistently outperformed the 1D CNN across all datasets, showing superior classification accuracy and overall performance. On both the KAPLCU, MLCU, and Hybrid datasets, DFNN demonstrated better precision, recall, and F1 scores, confirming its effectiveness in classifying land regions based on satellite imagery. Future work could involve exploring land cover classification using government datasets and developing labeled repositories from unlabeled satellite images to further expand research potential in this domain. © 2024 IEEE.