Abstract
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 elements, 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 a fast convergence rate and ease of implementation. 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 LULC classes in terms of classification accuracy, recall and precision. A maximum improvement of 6.03% on Sat 4 and 7.17% on Sat 6 in LULC classification is reached when the proposed human group-based PSO with LSTM is compared to individual LSTM, PSO with LSTM, and Human Group Optimization (HGO) with LSTM. Moreover, an improvement of 2.56% in accuracy is achieved, compared to the existing models, GoogleNet, Visual Geometric Group (VGG), AlexNet, ConvNet, when the proposed method is applied.
Highlights
In recent years, land-use and land-cover (LULC) classification using remote-sensing imagery plays an important role in many applications like land use planning, agricultural practice, forest management and biological resource [1,2,3]
The performance of the proposed model is compared with a few benchmark models: GoogleNet [26], 2 band Visual Geometric Group (VGG) [27], hyper parameter tuned VGG [27], 2 band AlexNet [27], hyper parameter tuned AlexNet [27], 2 band ConvNet [27], hyper parameter tuned ConvNet [27], AlexNet [28], ConvNet [28] and VGG [28], in order to find out its effectiveness
The proposed human group-based particle swarm optimization (PSO) with long short-term memory (LSTM) achieved a minimum of 0.01% and a maximum of 2.56% improvement in classification accuracy on Sat 4, Sat 6 and Eurosat datasets compared to the existing methodologies like GoogleNet [26], 2 band AlexNet [27], Hyper parameter tuned AlexNet [27], 2 band ConvNet [27], Hyper parameter tuned ConvNet [27], 2 band VGG [27], Hyper parameter tuned VGG [27], AlexNet [28], AlexNet-small [28], and VGG [28]
Summary
Land-use and land-cover (LULC) classification using remote-sensing imagery plays an important role in many applications like land use planning (growth trends, suburban sprawl, policy regulations and incentives), agricultural practice (conservation easements, riparian zone buffers, cropping patterns and nutrient management), forest management (harvesting, health, resource-inventory, reforestation and stand-quality) and biological resource (fragmentation, habitat quality and wetlands) [1,2,3]. LULC classification has a direct impact on atmospheric, soil erosion and water, while it is indirectly connected to global environmental problems [8,9] At this end, the remote sensing imagery and its processing has helped in delivering up-to date and large-scale information on surface conditions. Unlike the pixel-based technique, which classifies the pixels according to their spectral information, the object-based algorithms enclose semantic information not in the individual pixel but in groups of pixels with similar characteristics, such as color, texture, brightness and shape Both the spatial and spectral resolution are used in this latter case to segment and classify image features into meaningful objects [13]. These homogeneous objects are classified using traditional classification approaches such as the nearest neighbor, or using knowledge-based approaches and fuzzy classification logic [14]
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