论文标题
使用基于人类的粒子群体优化算法的土地利用和土地覆盖分类，并使用LSTM分类器进行混合预处理遥感图像
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
论文作者
论文摘要
使用遥感图像的土地使用和土地覆盖（LULC）分类在许多环境建模和土地使用清单中起着至关重要的作用。在这项研究中，提出了一种混合特征优化算法以及深度学习分类器，以改善LULC分类的性能，有助于预测野生动植物栖息地，恶化环境质量，随意的。选择遥感图像后，使用归一化和直方图均衡方法来提高图像的质量。然后，通过使用局部Gabor二进制模式直方图序列（LGBPHS），定向梯度（HOG）的直方图和Haralick纹理特征来完成混合优化，以从所选图像中提取特征。这种混合优化的好处是对颜色和灰度图像的高歧视能力和不变性。接下来，将基于人类的粒子群优化（PSO）算法应用于选择最佳特征，其优势是快速收敛速率且易于实现。选择最佳特征值之后，使用长期内存（LSTM）网络来对LULC类进行分类。实验结果表明，具有LSTM分类器的基于人类群体的PSO算法有效地区分了土地使用和土地覆盖类别的分类准确性，召回和精度。与现有模型Googlenet，VGG，Alexnet，Convnet相比，准确性提高了2.56％。
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.
