# Land Use and Land Cover Classification using a Human Group based Particle Swarm Optimization Algorithm with a LSTM classifier on hybrid-pre-processing Remote Sensing Images

arXiv: https://arxiv.org/abs/2008.01635 · HTML: https://mlsanity.com/paper/2008.01635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification](https://mlsanity.com/paper/2109.09484/) (2021)
- [Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms](https://mlsanity.com/paper/2308.11468/) (2023)

## Abstract (arXiv)

Land use and land cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the Local Gabor Binary Pattern Histogram Sequence (LGBPHS), the Histogram of Oriented Gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a Human Group based Particle Swarm Optimization (PSO) algorithm is applied to select the optimal features, whose benefits are fast convergence rate and easy to implement. After selecting the optimal feature values, a Long Short Term Memory (LSTM) network is utilized to classify the LULC classes. Experimental results showed that the Human Group based PSO algorithm with a LSTM classifier effectively well differentiates the land use and land cover classes in terms of classification accuracy, recall and precision. An improvement of 2.56% in accuracy is achieved compared to the existing models GoogleNet, VGG, AlexNet, ConvNet, when the proposed method is applied.