Sparse-FCM and deep learning for effective classification of land area in multi-spectral satellite images

Abstract

Remote sensing plays a major role in crop classification, land use classification, and land cover classification such that the information for the classification is assured with the help of the satellite images. This paper concentrates on the land use classification and proposes an optimization algorithm, called Firefly Harmony Search (FHS) for training the Deep Belief Neural Network (DBN). The FHS algorithm is the integration of the Firefly Algorithm and Harmony Search Algorithm (HSA), which tunes the weights of DBN to perform the multi-class classification. For the effective classification, the multispectral image is subjected to the sparse Fuzzy C-Means to form segments such that the feature extraction is effective, free from dimensionality issues and computational complexities. The features extracted from the segments of the multi-spectral images include vegetation indices and statistical features. Then, these features are fed to the DBN, which is tuned using the FHS algorithm for performing the land use classification. Experimentation using four datasets proves the effectiveness of the proposed multi-class classification approach. The accuracy, sensitivity, and specificity of the method are found to be 0.9317, 0.9568, and 0.0379, respectively, that is effective over the existing land use classification methods.

Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation

“…DataMed uses the Dataset Tag Suite (DATS) to store the generic metadata and to describe the data set effectively [18, 19]. The latest technology uses natural language processing and deep learning [20] using the deep mind, which is widely applicable in Microsoft’s Zo Chatbot and Google Deep Mind. Recently, the researchers are focused on using the semi-supervised and the unsupervised learning algorithm through the combination of non-annotated and annotated data [21, 22].…”

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

“…Therefore, reducing the complexity of the models is a crucial matter to be considered for future works. The recent study by [181], which proposes the Firefly Harmony Search (FHS) tuning algorithm for its Deep Belief Network model, also proves that simplifying the models can also improve the accuracy of classifications.…”