Deep Learning Approaches For Satellite Image Classification: A Review

Identifying vegetation from satellite images is very necessary for studies related to agricultural sector. Enhancing the quality and classification of satellite images is a challenge especially when study areas contain complex vegetation. The study aimed to enhance the image classification based on using extraction tool of the ArcGIS. In the present study, the classification of Landsat satellite images before and after extracting specific areas covering vegetation using the ArcGIS was compared. Two regions in Egypt, one with complex vegetation located at Siwa Oasis and the other one with simple vegetation located at Abu Simbel region, were used in this study. Then, several polygons were used to extract specific areas covering vegetation from the satellite images. Images were subsequently classified using Iso cluster unsupervised classification tool. The classification outcomes were compared between the original images before extraction with images after extraction. The results showed that extraction tool greatly enhanced the quality and classification of the image and it was possible to identify different types of vegetation.

Classification is a crucial stage in the processing of satellite images that influence considerably the quality of the result. A variety of methods is proposed in the literature for the purposes of image classification. They present many differences in their basic principles, thus in the quality of the results obtained. Therefore, a study of different classification methods seems to be essential. The classification of satellite images with conventional methods can be done in several ways using different algorithms. These algorithms can be divided into two main categories: supervised and non-supervised. Decision tree on the contrary is a machine learning tool. It is a plain model characterized by the simplicity of understanding and interpretation. This work aims firstly, to classify a high resolution Quickbird satellite image of an urban area by the decision tree method and compare it with the conventional classification algorithms in order to evaluate its efficiency. The methodology consists of two main stages: classification and evaluation of results. The second is based on the calculation of a number of statistical indices derived from the confusion matrix: the statistical parameter “kappa’ and the overall coefficient of precision.

Remote sensing techniques have been widely used for identification of land use and land cover features. Land information can be easily collected by classification of satellite images in the context of their use. In this paper study area has been classified into three classes i.e. settlement, trees and agricultural by classification of an image which has been enhanced using fusion of two images. The spatial and spectral resolutions of different satellite images provide better information with the aid of initial processing of image and fusion of both images. The satellite images fused together are multispectral IRS-P6 also called Resourcesat-1 satellite, on board LISS-III sensor provide image with spatial resolution of 23.5 m and an IRS-P5 also called Cartosat-1 satellite provides single band panchromatic image with spatial resolution of 2.5 m. Erdas Imagine 9.1 software has been used for image processing, fusion and supervised classification of the images. The Brovery, Multiplicative and Principal Component Analysis (PCA) method have been used for image fusion. The resultant images have been classified using the supervised classification with maximum likelihood parametric rule for information extraction and comparison between them in terms of their accuracy.

Remote sensing is one of the very important parts in the advance image processing which is used to acquiring satellite image to know about geo-spatial information. Remote sensing is widely used to monitor different resources available on earth surface, out of which the land used area monitoring is one of very important part. There are many techniques and methods are proposed for satellite image classification based on different theme. Here, in this paper theme of classification is land used and land area cover mapping using LISS-III satellite image. LISS-III satellite image have been classified in different area covered by water, forest, mangroves and developments by selecting the pixel based classification. Here multiple linear regressions is used on initial training data set, after evaluation the new features data set is applied to artificial neural network to check the impact of best training data set on the classification accuracy. The accuracy of classification algorithm is calculated using confusion matrix and Kappa coefficient. It is observed with help of confusion matrix and Kappa coefficient that the accuracy of ANN classifier without evaluated training data set is 90.19% and Kappa Coefficient is 0.8533 and when training data set evaluated with the help of multiple Linear regression then ANN accuracy is 98.74% and the Kappa Coefficient is 0.9766 which is improved and very good. So with the proposed method classification accuracy of ANN classifier is improvement for classification of LISS-III satellite image.

Natural Disasters are the events occurred within the earth system that leads to death or injury to humans and damage of valuable goods like buildings, communication systems, agricultural land, forest, natural environment. Natural disasters can be easily identified and the cause and effect of it can be minimized by the satellite image analysis. Satellite image analysis plays an essential role for environment and climate monitoring. Image classification is an essential process for performing the digital image examination in an efficient way. In satellite image classification process the grouping of image pixel values into pre-defined classes is done. Many satellite image classification methods were introduced for performing efficient disaster management. The analysis of two different problems is carried out in this paper to improvise the efficiency of determining the disaster management using satellite imagery.

With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.

Image processing is the field of signal processing where both the input and output signals are images. Images can be thought of as two-dimensional signals via a matrix representation. Image processing a is very important subject, and finds itself in such fields as photography, satellite imaging, medical imaging, and image compression, to name but a few. In the past, image processing was largely done using analog devices (Cheng et al., 2006). However, as computers became more powerful, processing shifted toward the digital domain. Like one-dimensional digital signal processing, digital image processing overcomes traditional analog problems such as noise, distortion during processing, inflexibility of system to change, and difficulty of implementation. Image retrieval, popularly referred to as content-based image retrieval is an emerging technology that allows a user to retrieve relevant images in an effective and efficient manner. Digital imaging has extensive applications in our daily lives and it is being used for several applications. Examples of imaging applications are in museums for archiving important images and manuscripts from art gallery and museum management. Many useful applications of imaging are found in security for tracking an intruder, crime prevention, law enforcement and object recognition in digital forensic. In particular, image retrieval is potentially useful in discovering brain activation patterns, in classifications and in diagnoses by comparing observed patterns with those of known diseases, leading to clinical applications. In biomedicine, content-based image retrieval is critically important in patient digital libraries, clinical diagnosis, clinical trials, searching for 2-D electrophoresis gels, and pathological slides. Most existing content-based image retrieval systems (Flickner et al., 1995; Gupta and Jain, 1997; Ma and Manjunath, 1997; Rubner, 1999 and Wang et al., 1998) are designed for general purpose picture libraries such as photos and graph. The storage, manipulation and analysis of the contents of digital images are essential requirements for the next generation of healthcare information infrastructure. The aim of this infrastructure is to bring timely health information to support communication among healthcare decision makers and communities at large. Among several healthcare services that can be provided with the aid of the emerging grid technology for ubiquitous access, image classification and diagnosis services are important. The ubiquitous access and

A machine learning-based Support vector machine (SVM) and Extended Local Binary Patterns (ELBP) methods have been used for the classification of satellite images among a set of 24 different classes. This work does not only classify the satellite image class this work is also capable of classifying 24 different class however to identify the features of those other classes like the human face, football, dog, etc. is also simple because these other classes have some exclusive features which can be simply to differentiate hence easy classification. In the case of the satellite image, the major issue is that different satellite images may have different features that make satellite image classification hard. Another issue is that safelight images are normally noise corrupted. The noise patterns of the wireless image are estimated using SVM Classifier, and with those estimated noise patterns removed by SVM algorithm of signal classification. This work first performs segmentation on the input test image to be classified then finds out the local binary patterns using the proposed ELBP method. The Extended LBP is needed because to differentiate the patterns of different satellite images and different other class images cannot be estimated with LBP only. Once extended features obtained SVM classifies the class of the test image. The method used in this work is ELBP-SVM and the Satellite image correct recognition obtain is 94%. The experimental results obtained on MATLAB 2018b and the results found are better than other available work t. classify satellite images.

Neural networks play an important role in satellite image classification. We know that the most common neural networks used in image classification tasks are convolutional neural networks (CNNs) (Boesch in Vision transformers (VIT) in image recognition—2022 guide, 2022). In this paper, we explored the influence of the spectral bands in image classification using the Vision Transformer (ViT). Convolution is a local operation, and a convolution layer typically models only the relationships between neighborhood pixels. Transformer is a global operation, and a transformer layer can model the relationships between all pixels. This motivated us to use ViT for satellite image classification. Sentinel-2 EuroSAT image dataset, which consists of 27,000 images in ten classes, is used for the experiment. ViT model is trained with three band dataset, Red-Green-Blue (RGB) and compared with ViT model trained with RGB along with Near InfraRed (NIR) and with multispectral satellite image dataset (13 bands). Experimental results shows that NIR band combined with RGB was able to produce more accurate results comparing to RGB alone, whereas 13 band dataset outperformed both RGB and RGB and NIR datasets.

Satellite image classification plays a crucial role in applications such as land cover mapping, urban planning, and environmental monitoring. This project focuses on the development of SpectrumNet, a custom Convolutional Neural Network (CNN) designed to classify satellite images from the EuroSAT dataset into ten distinct land-use categories. The primary goal is to improve the accuracy and efficiency of satellite image classification through the implementation of advanced preprocessing techniques, optimized training strategies, and multiple network configurations. By utilizing deep learning methodologies, SpectrumNet aims to provide reliable insights for real-world applications in remote sensing, geospatial analysis, and environmental studies. The project strives to enhance the ability to analyze and interpret large-scale satellite image data, offering valuable information for decision-making in various fields. Key Words: Deep Learning, Convolutional Neural Networks, EuroSAT, SpectrumNet, Geospatial Analysis, Land Cover Mapping, Environmental Monitoring, Urban Planning.

Satellite image classification is an important and challenging task in the modern technological age. Satellites can capture images of danger-prone areas with very little effort. However, the size and number of satellite images are very high when they are rapidly captured from space, and they require a huge amount of memory to store the data. In addition, keeping the satellite images private is another important task for security purposes. On-board, instant, accurate classification of a smaller number of satellite images is a challenging task, which is important to determine the specific condition of an area for instant monitoring. In the proposed hybrid approach, the captured images are kept secure, while the required training of the classification is done separately. Finally, the trained module is encrypted for use by the satellite to perform the on-board classification task. The Brakerski–Fan–Vercauteren (BFV)-based homomorphic encryption of EuroSAT satellite images is applied to store images in a cloud storage, where the privacy of the images can be maintained. Then, the decrypted images are used for training four transfer learning models (YOLOv8, YOLOv12, ResNet34, ResNet101, and a vision transformer classification. The best-trained module is encoded and encrypted again by using homomorphic encryption to limit the module to authorized devices. The encrypted module is decrypted and decoded to recover the trained module, which is used for instant classification of test images. Finally, the performance of the transfer learning models is evaluated from the test results. The vision transformer classifier achieved the highest accuracy of 99.65 % .

The segmentation and classification of high-resolution satellite images (HRSI) are useful approaches to extract information. In recent times, roads and buildings have been classified for analysis of urban areas in a better manner. Apart from these, healthy trees are also an important factor in HRSI, i.e. adjacent to roads, and vegetation. They reflect the area in an image as land cover. Other important information, shadow, is extracted from satellite images, which indicates the presence of trees and built-up areas such as buildings, flyovers, etc. In this article, a weighted membership-function-based fuzzy c-means with spatial constraints (WMFCSC) approach for automated satellite image classification is proposed. Initially, spatially fuzzy clustering is used to classify the satellite images in healthy trees with vegetation, roads, and shadows, which includes the information of spatial constraints. The road results of the classified image are still having non-road segments. Therefore, the proposed four intermediate stages (IS) are used to extract the road information, followed by the results of road areas of the WMFCSC approach. The framework of IS helps to remove the false road segments which are adjacent to roads and renovates the segmented roads due to the shadow effect. A final step of a hybrid WMFCSC-IS approach is used to extract the road network. The results of classified images confirm the effectiveness of the WMFCSC-IS approach for satellite image classification.

Nowadays solution of different scientific problems using satellite images, generally includes a classification procedure. Classification is one of the most important procedures used in remote sensing, because it involves a lot of mathematical operations and data preprocessing. The processing of information and combining of conflicting data is a very difficult problem in classification tasks. Nowadays many classification methods are applied in remote sensing. Classification of conflicting data has been a key problem, both from a theoretical and practical point of view. But a lot of known classification methods can not deal with highly conflicted data and uncertainty. The main purpose of this article is to apply proportional conflict redistribution rule (PRC5) for satellite image classification in conditions of uncertainty, when conflicting sources of evidence give incomplete and vague information. This rule can process conflicting data and combine conflicting bodies of evidence (spectral bands). Proportional conflict redistribution rule can redistribute the partial conflicting mass proportionally on non-empty sets involved in the conflict. It was noticed, that this rule can provide a construction of aggregated estimate under conflict. It calculates all partial conflicting masses separately. It was also shown, that proportional conflict redistribution rule is the most mathematically exact redistribution of conflicting mass to non-empty set. But this rule consists of difficult calculation procedures. The more hypotheses and more masses are involved in the fusion, the more difficult is to implement proportional conflict redistribution rule, therefore special computer software should be used. It was considered an example of practical use of the proposed conflict redistribution rule. It also was noticed, that this new approach to the application of conflict redistribution rule in satellite image classification can be applied for analysis of satellite images, solving practical and ecological tasks, assessment of agricultural lands, classification of forests, in searching for oil and gas.

Classification and analysis of high-resolution satellite images using conventional techniques have been limited. This is due to the complex characteristics of the imagery. These images are characterized by features such as spectral signatures, complex texture and shape, spatial relationships and temporal changes. In this research, we present the performance evaluation and analysis of deep learning approaches based on Convolutional Neural Networks and vision transformer towards achieving efficient classification of remote sensing satellite images. The CNN-based models explored include ResNet, DenseNet, EfficientNet, VGG and InceptionV3. The models were evaluated on three publicly available EuroSAT, UCMerced-LandUse and NWPU-RESISC45 datasets containing categories of images. The models achieve promising results in accuracy, recall, precision and F1-score. This performance demonstrates the feasibility of Deep Learning approaches in learning the complex and in-homogeneous features of the high-resolution remote sensing images.

Understanding local variability in malaria transmission risk is critically important when designing intervention or vaccine trials. Using a combination of field data, satellite image analysis, and GIS modeling, we developed a high-resolution map of malaria entomological inoculation rates (EIR) in The Gambia, West Africa. The analyses are based on the variation in exposure to malaria parasites experienced in 48 villages in 1996 and 21 villages in 1997. The entomological inoculation rate (EIR) varied from 0 to 166 infective bites per person per rainy season. Detailed field surveys identified the major Anopheles gambiae s.l. breeding habitats. These habitats were mapped by classification of a LANDSAT TM satellite image with an overall accuracy of 85%. Village EIRs decreased as a power function based on the breeding areas size and proximity. We use this relationship and the breeding habitats to map the variation in EIR over the entire 2500-km(2) study area.