Land use and land cover (LULC) classification are one of the important tasks to monitor the various land cover resources of the earth. In this research work, we have proposed a novel idea to classify the Landsat-8 multispectral remote sensing images. Machine learning and Deep learning algorithms play a very important tool to classify these multispectral images into various land cover classes such as cloud, water, forest, urban area, plantation, and cropland. In this paper, Long Short-Term Memory (LSTM) of Recurrent neural network (RNN) is used to achieve high-level classification accuracy and solve the memory problem issues which can be occurred at internal state. Our proposed work proved that; it resolved the gradient problem of recurrent neural networks. The Real-time experiment was conducted by Landsat-8 image and proposed work compared with traditional RNN and fuzzy c-means (FCM). The proposed method achieved 97.58% classification accuracy for the Landsat-8 image.

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