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@article{166751,
        author = {Kanagala Koushik and Sheelam sai  manoj},
        title = {Unleashing the Power of CNN-LSTM: Enhancing Remote Sensing Image Captioning for Unprecedented Results},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1836-1840},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166751},
        abstract = {Remote sensing picture captioning is a enormous project in the subject of computer imaginative and prescient as it aids in information and decoding far off sensing photos. picture captioning entails mechanically generating herbal language descriptions based totally at the visible content material discovered in an image. To tackle this challenge, gadget getting to know strategies, specifically convolutional neural networks and LSTM models, have been widely hired. CNN fashions are well-appropriate for reading picture statistics, as they can research spatial features from the entered records and classify distinct forms of objects or scenes.},
        keywords = {far flung sensing, photo captioning, pc imaginative and prescient, Convolutional neural networks (CNN), long short term memory (LSTM), natural language processing (NLP), machine getting to know, Deep getting to know, visual content expertise, Spatial capabilities, item recognition, Scene type, automated description era, photo interpretation, Neural network fashions.},
        month = {July},
        }