Aerial Image Classification Research Articles

Deep learning-based aerial image classification model using inception with residual network and multilayer perceptron

At present times, unmanned aerial vehicles (UAVs) have become popular owing to the advantages such as scalable, versatile, autonomous, and inexpensive. The aerial image classification process has attracted more attention among the UAV-enabled surveillance system facilitating objective recognition, tracking process and considerably improving the visual surveillance results. This paper introduces effective deep learning-based aerial image classification using Inception with Residual Network v2 and multilayer perceptron (DLIRV2-MLP). The presented DLIRV2-MLP method includes a series of operations such as pre-processing, feature extraction, and classification. Primarily, the UAVs are used for the collection of aerial images by the use of imaging sensors exist in it. Followed by, the Inception with ResNet v2 based feature extractor is utilized to derive a useful set of feature vectors from the aerial image. Finally, the classification of aerial images using the derived feature vectors takes place by the use of MLP model. Here, the MLP classifier is used as the final layer of the Inception with ResNet v2 model in place of the softmax layer to improve the classification performance. The efficacy of the DLIRV2-MLP method is validated using a benchmark aerial image dataset and the resultant experimental values highlighted the effectiveness of the DLIRV2-MLP method with 90% and above in accuracy and precision. The problem of existing method faces some issues in computation whereas the aerial image classification with DLIRV2-MLP gives efficient computation while comparing with the existing method.

Dual-View Stereovision-Guided Automatic Inspection System for Overhead Transmission Line Corridor

Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected according to the generated 3D points of the transmission line corridor. Tests on both corresponding datasets and practical 220-kV transmission line corridors are conducted. The experimental results of different modules reveal that our proposed system can be applied in practical inspection environments and has good performance.

S-MAT: Semantic-Driven Masked Attention Transformer for Multi-Label Aerial Image Classification.

Multi-label aerial scene image classification is a long-standing and challenging research problem in the remote sensing field. As land cover objects usually co-exist in an aerial scene image, modeling label dependencies is a compelling approach to improve the performance. Previous methods generally directly model the label dependencies among all the categories in the target dataset. However, most of the semantic features extracted from an image are relevant to the existing objects, making the dependencies among the nonexistant categories unable to be effectively evaluated. These redundant label dependencies may bring noise and further decrease the performance of classification. To solve this problem, we propose S-MAT, a Semantic-driven Masked Attention Transformer for multi-label aerial scene image classification. S-MAT adopts a Masked Attention Transformer (MAT) to capture the correlations among the label embeddings constructed by a Semantic Disentanglement Module (SDM). Moreover, the proposed masked attention in MAT can filter out the redundant dependencies and enhance the robustness of the model. As a result, the proposed method can explicitly and accurately capture the label dependencies. Therefore, our method achieves CF1s of , , and on three multi-label aerial scene image classification benchmark datasets: UC-Merced Multi-label, AID Multi-label, and MLRSNet, respectively. In addition, extensive ablation studies and empirical analysis are provided to demonstrate the effectiveness of the essential components of our method under different factors.

Few-Shot aerial image classification with deep economic network and teacher knowledge

ABSTRACT Deep learning has achieved excellent achievements and has become the mainstream in the field of aerial image classification. While obtaining remarkable success, deep learning-based approaches are notoriously dependent on large amounts of labelled data. Few-shot learning uses existing knowledge to learn from few samples and quickly generalizes to new tasks. In this work, we proposed the few-shot learning with deep economic network and teacher knowledge for aerial image classification. Firstly, we performed simplification twice to reduce large-scale parameters and computational effort in deep networks. In the first simplification, the redundancy in feature inputs’ main components is reduced, and the implicit information in redundant components is extracted instead of directly discarding the redundant components. The channel and spatial redundancies in deeper layers’ inputs are reduced in the second simplification. Secondly, the teacher knowledge guides the random sampling and uses limited samples to improve classification performance. We conducted extensive experiments on NWPU-RESISC45, RSD46-WHU, and UC Merced datasets. The experimental results reveal that the proposed method has better classification accuracy, fewer network parameters, and less computational effort. Experiments on miniImageNet, FC100, CUB, and cross-domain datasets show that our method also maintains advanced classification accuracy on few-shot image classification benchmarks.

Swarm Intelligence with Deep Transfer Learning Driven Aerial Image Classification Model on UAV Networks

Nowadays, unmanned aerial vehicles (UAVs) have gradually attracted the attention of many academicians and researchers. The UAV has been found to be useful in variety of applications, such as disaster management, intelligent transportation system, wildlife monitoring, and surveillance. In UAV aerial images, learning effectual image representation was central to scene classifier method. The previous approach to the scene classification method depends on feature coding models with lower-level handcrafted features or unsupervised feature learning. The emergence of convolutional neural network (CNN) is developing image classification techniques more effectively. Due to the limited resource in UAVs, it can be difficult to fine-tune the hyperparameter and the trade-offs amongst computation complexity and classifier results. This article focuses on the design of swarm intelligence with deep transfer learning driven aerial image classification (SIDTLD-AIC) model on UAV networks. The presented SIDTLD-AIC model involves the proper identification and classification of images into distinct kinds. For accomplishing this, the presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model in which the hyperparameter optimization process is performed by the use of salp swarm algorithm (SSA). In addition, a cascaded long short term memory (CLSTM) model is executed for classifying the aerial images. At last, seeker optimization algorithm (SOA) is applied as a hyperparameter optimizer of the CLSTM model and thereby results in enhanced classification accuracy. To assure the better performance of the SIDTLD-AIC model, a wide range of simulations are implemented and the outcomes are investigated in many aspects. The comparative study reported the better performance of the SIDTLD-AIC model over recent approaches.

Deep Learning Techniques to Classify the Aerial Images with Gabor Filter

Abstract: Aerial Images are a valuable data source for earth observation, can help us to measure and observe detailed structures on the Earth’s surface. Aerial images are drastically growing. This has given particular urgency to the quest for how to make full use of ever-increasing Aerial images for intelligent earth observation. Hence, it is extremely important to understand huge and complex Aerial images. Aerial image classification, which aims at labeling Aerial images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, Aerial image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, recent achievements regarding deep learning for scene classification of Aerial images is still lacking. Keywords: Deep learning, Aerial image, CNN, Gabor Filter

DEEP CONVOLUTION NEURAL NETWORKS WITH RESNET ARCHITECTURE FOR SPECTRAL-SPATIAL CLASSIFICATION OF DRONE BORNE AND GROUND BASED HIGH RESOLUTION HYPERSPECTRAL IMAGERY

Abstract. Drones have been of vital importance in the fields of surveillance, mapping, and infrastructure inspection. Drones have played a vital role in acquiring high-resolution images and with the present need for precision farming, drones have helped in crop classification and monitoring various crop patterns. With the recent advancement in computational power and development of robust algorithms to carry out deep feature learning and neural network, based learning such techniques have regained prominence in contemporary research areas such as classification of common 2-D and 3-D images, object detection, etc. In our research, we propose a deep convolutional neural network architecture (CNN) for the classification of aerial images captured by drones and high-resolution Terrestrial Hyperspectral (THS or HSI) which includes 6-layers and with weights optimized along with the input layer, the convolutional layer, the max-pooling layer, the fully connected layer, softmax probability classifier, and the output layer. We have acquired THS (using Cubert-GmbH data) and drone agricultural data of seasonal crops sowed during the months of March-June for the year 2017. Crop patterns include Cabbage, Eggplant, and Tomato with varying nitrogen concentrations in the region of Bangalore, Southern India. To study the influence and impact of CNN, the ResNets model has been applied. ResNets model and architecture are combined with a deep learning network followed by a recurrent neural learning network model (RCNN). The HSI input layer with corresponding ground truth data for the region is fed into the ResNets model with a spectral and spatial residual network for the 7*7*139 input Hyperspectral Imagery (HSI) volume. The network includes two spectral and two spatial residual blocks. An average pooling layer and a fully connected layer transform into a 5*5*24 spectral-spatial feature volume further to a single output feature vector. At present we use an RMSProp optimizer for error loss minimization which when applied to the drone data was able to achieve an overall accuracy of 97.16%. Similarly, for cabbage, eggplant and tomato acquired through the same method we achieved overall accuracy at 87.619%, 89.25%, and 80.566% respectively in comparison to ground truth labels. Drones and ground-based datasets equipped with good computational techniques have become promising tools for improving the quality and efficiency of precision agriculture today.

Privacy protected internet of unmanned aerial vehicles for disastrous site identification

AbstractDisastrous site identification through the internet of UAVs is a current research area that aims to improve data sharing by connecting servers to the internet. Internet‐connected unmanned aerial vehicles (UAVs) for aerial image classification necessitated the sharing of large datasets between the number of connected flying machines. The artificial intelligence model training to predict images need to compromise data privacy, consume a lot of energy, and high requirement of data communication. This article aims to propose and implement federated deep learning trained aerial image classification for disastrous sites through the internet of UAVs. The proposed model provides privacy‐preserving and resource efficient deep learning by sharing only models rather than large datasets of images. Multisystem client–server federated learning UAV architecture was implemented and comparatively analyzed on basis of parameters namely, training–testing accuracy, train‐testing loss, RAM‐CPU utilization.

Semantic Understandings for Aerial Images via Multigrained Feature Grouping

Aerial images play a key role in remote sensing as they can provide high-quality surface object information for continuous communication services. With advances in UAV-aided data collection technologies, the volume of aerial images has been greatly promoted. To this end, semantic understandings for these images can significantly improve the quality of service for smart devices. Recently, the multilabel aerial image classification (MAIC) task has been widely researched in academics and applied in industries. However, existing MAIC methods suffer from suboptimal performance as objects are located in different sizes and scales. To address these issues, we propose a novel multigrained semantic grouping model for aerial image learning, named MSGM. First, image features presented by the backbone are sent to spatial pyramid convolutional layers which extract the instances in a parallel manner. Then, three grouping mechanisms are designed to integrate the instances from the pyramid framework. In addition, MSGM builds a concept graph to represent the label relationship. MSGM resorts to the graph convolutional network to learn the concept graph directly. We extensively evaluate MSGM on two benchmark aerial image datasets, the commonly used UCM dataset, and the high-resolution DFC15 dataset. Quantitative and qualitative results support the effectiveness of the proposed MSGM.

Fine Crop Classification Based on UAV Hyperspectral Images and Random Forest

The classification of unmanned aerial vehicle hyperspectral images is of great significance in agricultural monitoring. This paper studied a fine classification method for crops based on feature transform combined with random forest (RF). Aiming at the problem of a large number of spectra and a large amount of calculation, three feature transform methods for dimensionality reduction, minimum noise fraction (MNF), independent component analysis (ICA), and principal component analysis (PCA), were studied. Then, RF was used to finely classify a variety of crops in hyperspectral images. The results showed: (1) The MNF–RF combination was the best ideal classification combination in this study. The best classification accuracies of the MNF–RF random sample set in the Longkou and Honghu areas were 97.18% and 80.43%, respectively; compared with the original image, the RF classification accuracy was improved by 6.43% and 8.81%, respectively. (2) For this study, the overall classification accuracy of RF in the two regions was positively correlated with the number of random sample points. (3) The image after feature transform was less affected by the number of sample points than the original image. The MNF transform curve of the overall RF classification accuracy in the two regions varied with the number of random sample points but was the smoothest and least affected by the number of sample points, followed by the PCA transform and ICA transform curves. The overall classification accuracies of MNF–RF in the Longkou and Honghu areas did not exceed 0.50% and 3.25%, respectively, with the fluctuation of the number of sample points. This research can provide reference for the fine classification of crops based on UAV-borne hyperspectral images.

Remote sensing image registration based on the improved attention neighborhood consistency network

Image registration plays an important role in surface detection, aerial image classification, and satellite image fusion. However, the existing image registration frameworks pay less attention to local feature information. Additionally, such frameworks have insufficient extraction of important channel and spatial information and ambiguous matching. To address these issues, we propose an end-to-end remote sensing image registration framework based on the improved attention neighborhood consistency network. Our network improves the robustness of the rotation distortion region by employing an asymmetric convolution block in the feature extraction stage, which is augmented by two one-dimensional convolution kernels with square convolution kernels. Moreover, we use a 1 × 1 convolution to adjust the residual network channel to retain global information, realize feature reuse, and obtain multiscale image information. Next, the attention mechanism is improved by utilizing skip connections to resolve the problems of high resolution and image heterogeneity issues in remote sensing images. This allows the framework to quickly focus on important regional features and improve the network’s ability to distinguish and localize image features. In the feature matching stage, a bidirectional cross-correlation algorithm is used to establish the correspondence between the two pictures and match the corresponding points are matched. At the same time, the multilayer perceptron (MLP) structure is introduced into the neighborhood consensus network to eliminate the noise generated by mismatching. Further, it establishes a strong matching locality, prompts information aggregation at different positions and different channels, reduces registration deviation, and enhances matching robustness. The experimental results show that, overall, the algorithm outperforms comparable algorithms, extracting more accurate feature points and efficiently reducing the mismatch rate. The algorithm put forward effectively functions in a variety of situations, exhibiting high efficiency and high accuracy.

Optimal Squeeze Net with Deep Neural Network-Based Arial Image Classification Model in Unmanned Aerial Vehicles

In present times, unmanned aerial vehicles (UAVs) are widely employed in several real time applications due to their autonomous, inexpensive, and compact nature. Aerial image classification in UAVs has gained significant interest in surveillance systems that assist object detection and tracking processes. The advent of deep learning (DL) models paves a way to design effective aerial image classification techniques in UAV networks. In this view, this paper presents a novel optimal Squeezenet with a deep neural network (OSQN-DNN) model for aerial image classification in UAV networks. The proposed OSQN-DNN model initially enables the UAVs to capture images using the inbuilt imaging sensors. Besides, the OSQN model is applied as a feature extractor to derive a useful set of feature vectors where the coyote optimization algorithm (COA) is employed to optimally choose the hyperparameters involved in the classical SqueezeNet model. Moreover, the DNN model is utilized as a classifier that aims to allocate proper class labels to the applied input aerial images. Furthermore, the usage of COA for hyperparameter tuning of the SqueezeNet model helps to considerably boost the overall classification performance. For examining the enhanced aerial image classification performance of the OSQN-DNN model, a series of experiments were performed on the benchmark UCM dataset. The experimental results pointed out that the OSQN-DNN model has resulted in a maximum accuracy of 98.97% and a minimum running time of 1.26mts.

Quantifying the impacts of land cover change on catchment-scale urban flooding by classifying aerial images

Stormwater urban drainage systems are historically designed to mitigate certain recurrence interval flooding events. However, increases in impervious land cover due to urban redevelopment enhance the surface imperviousness and then magnifies urban floods over the design criteria by elevating flooding peaks and volumes. In this paper, the object-based image classification was developed to evaluate the impervious land cover impacts on system peak flooding rate and total flooding volume. The object-based aerial image classification quantified the spatiotemporal changes in impervious surfaces with an average accuracy of 92%. A drainage model, based on a stormwater drainage system in Salt Lake City, Utah, USA, was calibrated and validated under four storm events with an average of Nash-Sutcliffe efficiency of 0.69 and 0.46 for calibration and validation outcomes. Results show that impervious areas increased up to 32% under impacts induced by urban redevelopment from 1950 to 2018. The land cover imperviousness resulted in a maximum of 575% and 753% growth in flooding peak and volume, respectively, under storm events from 10-year to 100-year return periods. Implications of this research seek to inform homeowners and engineers of the flooding risks in human-altered landscapes based on remote sensing image classification.

Deep Neural Network Based Vehicle Detection and Classification of Aerial Images

The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems. Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes. It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle’s modest size, other objects with similar visual appearances are not correctly addressed. A robust algorithm for vehicle detection and classification has been proposed to overcome the limitation of existing techniques in this research work. We propose an algorithm based on Convolutional Neural Network (CNN) to detect the vehicle and classify it into light and heavy vehicles. The performance of this approach was evaluated using a variety of benchmark datasets, including VEDAI, VIVID, UC Merced Land Use, and the Self database. To validate the results, various performance parameters such as accuracy, precision, recall, error, and F1-Score were calculated. The results suggest that the proposed technique has a higher detection rate, which is approximately 92.06% on the VEDAI dataset, 95.73% on the VIVID dataset, 90.17% on the UC Merced Land dataset, and 96.16% on the Self dataset.

Rethinking Crowdsourcing Annotation: Partial Annotation With Salient Labels for Multilabel Aerial Image Classification

Annotated images are required for supervised model training and evaluation in aerial image classification. Manually annotating images is arduous and expensive, especially for aerial images, which often cover a large land area with multiple labels. A recent trend for conducting such annotation tasks is through crowdsourcing, where images are annotated by volunteers or paid workers (e.g., annotation volunteers for Open Street Map) online from scratch. However, for crowdsourcing image annotations, the quality cannot be guaranteed, and incompleteness and incorrectness are two major concerns. To address such concerns, we have a rethinking of crowdsourcing annotations: Our simple hypothesis is that if annotators only partially annotate multi-label images with salient labels they are confident in, there will be fewer annotation errors and annotators will spend less time on uncertain labels. As a pleasant surprise, with the same annotation budget, we show that a multi-label aerial image classifier supervised by images with salient annotations can outperform models supervised by fully annotated images. Our contributions are 2-fold: An active learning way is proposed to acquire salient labels for multi-label aerial images; and a novel Adaptive Temperature Associated Model (ATAM) specifically using partial annotations is proposed for multi-label aerial image classification. When tested on practical crowdsourcing aerial data, the Open Street Map (OSM) dataset, the proposed ATAM can achieve higher accuracy than state-of-the-art classification methods trained on fully annotated images. The proposed idea is promising for crowdsourcing aerial image annotation. Our code will be publicly available.

Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification Model in IoT Enabled UAV Networks

Unmanned aerial vehicles (UAVs) can be placed effectively in offering high-quality services for Internet of Things (IoT) networks. It finds use in several applications such as smart city, smart healthcare, surveillance, environment monitoring, disaster management, etc. Classification of images captured by UAV networks, i.e., aerial image classification is a challenging task and can be solved by the design of artificial intelligence (AI) techniques. Therefore, this article presents an Earthworm Optimization with Deep Transfer Learning Enabled Aerial Image Classification (EWODTL-AIC) model in IoT enabled UAV networks. The major intention of the EWODTL-AIC technique is to effectually categorize different classes of aerial images captured by UAVs. The EWODTL-AIC technique initially employs AlexNet model as feature extractor for producing optimal feature vectors. Followed by, the hyperparameter values of the AlexNet model are decided by the utilization of earthworm optimization (EWO) algorithm. At last, the extreme gradient boosting (XGBoost) model is employed for the classification of aerial images. The experimental validation of the EWODTL-AIC model is performed using benchmark dataset. The extensive comparative analysis reported the better outcomes of the EWODTL-AIC technique over the other existing techniques.

Multi-View Urban Scene Classification with a Complementary-Information Learning Model

Traditional urban scene-classification approaches focus on images taken either by satellite or in aerial view. Although single-view images are able to achieve satisfactory results for scene classification in most situations, the complementary information provided by other image views is needed to further improve performance. Therefore, we present a complementary information-learning model (CILM) to perform multi-view scene classification of aerial and ground-level images. Specifically, the proposed CILM takes aerial and ground-level image pairs as input to learn view-specific features for later fusion to integrate the complementary information. To train CILM, a unified loss consisting of cross entropy and contrastive losses is exploited to force the network to be more robust. Once CILM is trained, the features of each view are extracted via the two proposed feature-extraction scenarios and then fused to train the support vector machine classifier for classification. The experimental results on two publicly available benchmark data sets demonstrate that CILM achieves remarkable performance, indicating that it is an effective model for learning complementary information and thus improving urban scene classification.

MINet: Multilevel Inheritance Network-Based Aerial Scene Classification

Scene classification of aerial images is the basis of automatic recognition of complex scenes, and it is also a challenging computer vision task. In recent years, with the rapid development of deep learning, the semantic feature extraction method based on a convolutional neural network (CNN) has made great progress. Moreover, a recent study indicates that combining the semantic information of deep-layer features with the detailed texture information of shallow-layer features in CNN can further improve the performance of classification. In this letter, an end-to-end multilevel feature-based network named multilevel inheritance network (MINet) is proposed for aerial scene classification. First, the feature extraction module based on the feature pyramid network (FPN) is used to get multilevel feature maps. In the process of merging shallow features, high-level semantics of deep-layer are inherited. Then, an attention mechanism is added after the multilevel features to reduce the interference of redundant information and noise. Finally, we use a feature fusion module to automatically learn the weight of each feature layer and make a comprehensive decision. The effectiveness of the proposed method is verified in AID, WHU-RS19 and NWPU-RESISC45 datasets. Results show that the proposed method achieves competitive classification accuracy.

Multilabel Aerial Image Classification With Unsupervised Domain Adaptation

Deep learning (DL) methods are promising for the multilabel aerial image classification (MAIC) task. However, current DL methods face a common problem: the need for large multilabeled datasets. Collecting and annotating raw aerial image datasets can be extremely time- and labor-consuming. To address this concern in MAIC, domain adaptation (DA) provides a novel solution by transferring the knowledge learned from a label-rich dataset (i.e., the source domain) to a label-scarce dataset (i.e., the target domain), while current DA models are mainly designed for single-labeled tasks. In this article, we propose a novel end-to-end MAIC model based on DA techniques, named DA-MAIC. To the best of our knowledge, this article for the first time integrates DA to tackle the label scarcity problem in the MAIC task. Specifically, the proposed DA-MAIC is composed of two main parts: the image classifier and the domain classifier. The image classifier captures task-discriminative features based on the graph convolutional network (GCN) to predict multiple image labels; and the domain classifier extracts domain-invariant representations, which mitigates the domain shift between two underlying distributions. We extensively evaluate the proposed DA-MAIC from different perspectives on three benchmark datasets, including the commonly used UCM dataset, the high-resolution AID dataset, and the recently proposed DFC15 dataset. Both quantitative and qualitative results support that the proposed DA-MAIC can generalize the source domain knowledge to new scenarios and substantially improve the classification performance on the target domain task.

An automated deep learning based satellite imagery analysis for ecology management

Ecology is the methodical study of biodiversity which affecting natural life and habitats. Due to the anthropogenic pressure on the atmosphere, there is an upraised threat to wild animals and other habitats of the ecological atmosphere. So, there is a need for efficient ecology management models to map and save nature resources. At the same time, the use of satellite imagery analysis is an effective tool for determining important details on earth resources and the platform. It finds useful for proficient ecology management, such as land use detection, forest fire detection, environment planning, and so on. Earlier satellite imagery classification approaches mainly depend upon feature coding approaches which has limited capabilities and yield mediocre outcomes. The recent developments of deep learning models made the image classification highly effective. In this view, this paper presents a new parameter tuned deep learning based EfficientNet model with Variational Autoencoder (PTDLEN-VAE) model for satellite imagery analysis on ecology management. The presented PTDLEN-VAE model includes a series of operations namely pre-processing, feature extraction, and classification. Primarily, the satellite images are preprocessed to improve the contrast level of the image. Followed by, the PTDLEN based feature extractor is utilized to derive a useful set of feature vectors from the aerial image. Besides, the improved krill herd optimization (IKHO) algorithm is applied for the parameter tuning of the EfficientNet model. Finally, the classification of aerial images using the derived feature vectors takes place by the use of the VAE model.The efficacy of the PTDLEN-VAE model is validated using a benchmark aerial image dataset and the resultant experimental values highlighted the effectiveness of the PTDLEN-VAE model interms of precision, recall, F1-score, F2-score, and computation time.