Objects In Satellite Images Research Articles

Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA

In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.

An Analysis: Progress in Identifying Objects, Classification and Segmenting them in Satellite Images

Abstract: Recognizing and differentiating items in satellite pictures is crucial for different goals, such as city planning and disaster response. Recent developments in machine learning, particularly deep learning, and the abundance of high-quality satellite images have significantly boosted progress in this area. CNNs have become essential for precisely identifying and categorising elements depicted in satellite images. These models use convolution and pooling methods on a local scale to identify local patterns and far-reaching connections, ultimately enhancing object detection and segmentation accuracy. This review offers a detailed summary of the advanced methods used to identify and separate objects in satellite images. The article discusses the importance of precise object detection and segmentation in today's dynamic environment, showcasing the progress achieved through deep learning methodologies. The article explores the difficulties of pinpointing key and distinct areas in satellite pictures and shows how deep learning models tackle this issue by detecting the connections between objects and various facets of the surroundings.

Poster] Aircraft Detection and State Estimation in Satellite Images

Unidentified flying objects are aircraft that do not continuously broadcast ADS-B. They pose a risk to air traffic safety. In this study, we introduce a method for detecting and estimating the state of aircraft in Sentinel-2 multispectral satellite images. We construct a dataset of 579 ADS-B annotated aircraft from 69 Sentinel-2 images. A CNN is trained on the dataset to aircraft state vector i.e. position, velocity, heading, and altitude. This work allows real-time monitoring of flying objects in satellite images.

SHAP-Based Interpretable Object Detection Method for Satellite Imagery

There is a growing need for algorithms to automatically detect objects in satellite images. Object detection algorithms using deep learning have demonstrated a significant improvement in object detection performance. However, deep-learning models have difficulty in interpreting the features for inference. This difficulty is practically problematic when analyzing earth-observation images, which are often used as evidence for public decision-making. In addition, for the same reason, it is difficult to set an explicit policy or criteria to improve the models. To deal with these challenges, we introduce a feature attribution method that defines an approximate model and calculates the attribution of input features to the output of a deep-learning model. For the object detection models of satellite images with complex textures, we propose a method to visualize the basis of inference using pixel-wise feature attribution. Furthermore, we propose new methods for model evaluation, regularization, and data selection, based on feature attribution. Experimental results demonstrate the feasibility of the proposed methods for basis visualization and model evaluation. Moreover, the results illustrate that the model using the proposed regularization method can avoid over-fitting and achieve higher performance, and the proposed data selection method allows for the efficient selection of new training data.

Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis

Deep learning and machine learning are the top ranking techniques applied in objects classification in remote sensing data. We have conducted a meta-analysis and find out that feature selection is an important achievement in Machine Learning algorithms however, the following challenges were identified; Machine learning need large datasets for training and satellite images contain a lot of noise which may be classify as an object so it is not suitable for object detection in satellite images, Detection accuracy in machine learning depend on the quality of training datasets and finally Biased feature selection may led to the incorrect classification of objects in satellite images. While Most of the deep learning techniques suffer from data preprocessing problems especially when applying in satellite images because satellite images contain a lot of noise. Therefore the requirement of quality and quantity of training datasets is very high. The designed, development, improvement and adjustment of deep learning techniques to suit a specific research is still rely on the experience of the developer which is also a challenging issue. Application of deep learning techniques in remote sense data are still in an infant state because based on our review only few numbers of articles are published from Africa countries. We have suggested that quantum computational intelligence to be applied in remote sensing data analysis.

Multi-class Object Detection Model in Satellite Images Using Convolutional Neural Network

The accurate multi-detection of objects in satellite images has become very essential due to the high criminal activities that posed security threat to humanity all over the world. However, there are significant limitations of traditional methods of multi-object detection such as matching based techniques and object based image analysis. Although Convolutional neural network and image processing techniques has been proved to be essential fields in so many applications of computer vision specifically multi-object detection, multi-object classification, object retrieval, object recognition and object segmentation in a digital image or video, however, multi-object detection especially in satellite images suffer from problems such as shadow, camouflage, and occlusion. The aim of this research work was to design a robust multi-class object detection model in satellite images using image processing techniques and convolutional neural network with a particular concern on image preprocessing, image denoising and image enhancement to enable address the issue of noise in satellite images. The Satellite image that are propose for this model is LandSat-8, because it is free access for research and have a tract record in terms of consistency. The proposed model applied supervised learning algorithm for training different samples of labeled data for the model to enable the system detect vegetation, water bodies, road networks and building. This research will enable the government to know the positions as well as the coordinates of every thick forest, drainage, road networks and buildings in the forest for security reasons. It is at the heart of this research to pave away for the full implementation of this model using either MATLAB or Python Programming.

Searching and describing objects in satellite images on the basis of modeling reasoning

The article presents an approach to a problem of contextual search and description of objects in raster satellite images, which consists in modeling reasoning on the basis of structured cases. As a result of image processing, an adjacency graph of color regions is constructed. The object is characterized by color, attributes of the form of segments of the border and the shape of the object as a whole. A structured case is represented in the form of a beam graph, whose arcs are ordered according to a positive bypass of the region boundaries. Using a graph matching algorithm, occurrences of cases stored in the system database are detected in the analyzed image. When the occurrence is detected, a case-based inference rule is applied. The degree to which an object belongs to a certain class depends not only on the properties of the object itself, but also on the reliability of the surrounding objects. The contextual search strategy contains stages of recursion and iteration. In contrast to neural network technologies, the proposed approach allows one not only to classify image objects, but also to form their structured descriptions. In addition, the classification decision issued by the system has a reasoned justification. The results of the experiment show that reasoning based on structured cases allows refining the results of classification and increasing the reliability of object recognition in satellite images.

APPLICATION OF KOHONEN SELF-ORGANIZING MAP TO SEARCH FOR REGION OF INTEREST IN THE DETECTION OF OBJECTS

Today, there is a serious need to improve the performance of algorithms for detecting objects in images. This process can be accelerated with the help of preliminary processing, having found areas of interest on the images where the probability of object detection is high. To this end, it is proposed to use the algorithm for distinguishing the boundaries of objects using the Sobel operator and Kohonen self-organizing maps, described in this paper and shown by the example of determining zones of interest when searching and recognizing objects in satellite images. The presented algorithm allows 15–100 times reduction in the amount of data arriving at the convolutional neural network, which provides the final recognition. Also, the algorithm can significantly reduce the number of training images, since the size of the parts of the input image supplied to the convolution network is tied to the image scale and equal to the size of the largest recognizable object, and the object is centered in the frame. This allows to accelerate network learning by more than 5 times and increase recognition accuracy by at least 10 %, as well as halve the required minimum number of layers and neurons of the convolutional network, thereby increasing its speed.

Review Study on Digital Image Processing and Segmentation

Image segmentation is in fact one of the most fundamental approach of digital image processing. In image processing, segmentation playa an important role. It may be defined as partitioning an image into meaning full regions or objects. In other words we can say that process of segmentation keeps on dividing an image into its constitute sub parts. The level to which the subdivision is carried on depends on type of problem to be solved by researchers. This segmentation process continues unless area of interest is isolated. Set of segment or set of contours that are extracted from the image is the main result of image segmentation. There are various application of image segmentation like locating tumors or other pathologies, measuring tissue volume, surgery aided by computer, treatment and planning, study of various anatomical structure, locating objects in satellite images, fingerprint. There are various types of generalized algorithm and methodology that are developed for image segmentation. Some common technique of image segmentation such as edge detection, thresh holding, region growing and clustering are taken for this study. In fact segmentation algorithm are based on two properties similarity and discontinuity. This paper concentrates on the various methods that are widely used to segment the image.

REGION GROWING IMAGE SEGMENTATION ON LARGE DATASETS USING GPU

Image segmentation is an important image processing, and it seems everywhere if we want to analyze what inside the image. There are varieties of applications of image segmentation such as the field of filtering noise from image, medical imaging, and locating objects in satellite images and in automatic traffic control systems, machine vision in problem of feature extraction and in recognition. This paper focuses on accelerating the image segmentation mechanism using region growing algorithm inside GPU (Graphical Processing Unit). In region growing algorithm, an initial set of small areas are iteratively merged according to similarity constraints. We have started by choosing an arbitrary seed pixel and compare it with neighboring pixels. Region is grown from the seed pixel by adding in neighboring pixels that are similar, increasing the size of the region. When the growth of one region stops we simply choose another seed pixel which does not yet belong to any region and start again. This whole process is continued until all pixels belong to some region. If any of the segment makers has the fusion cost lower than the maximum fusion cost (a given threshold), it is selected to grow. Avoid information overlapping like two threads attempting to merge its segment with the same adjacent segment. Experiments have demonstrated that the proposed shape features do not imply in a significant change of the segmentation results, as long as the algorithm’s parameters are properly adjusted. Moreover, experiments for performance evaluation indicated the potential of using GPUs to accelerate this kind of application. For a simple hardware (GeForce 630M GT), the parallel algorithm reached a maximum speed up of approximately 20-30% for different datasets. Considering that segmentation is responsible for a significant portion of the execution time in many image analysis applications, especially in object-oriented analysis of remote sensing images, the experimentally observed acceleration values are significant. Two variants of PBF (Parallel Best Fitting) and PLMBF (Parallel Local Mutual Best Fitting) have been used to analyze the best merging cost of the two segments. It has been found that the PLMBF has been performed better than PBF. It should also be noted that these performance gains can be obtained with low investment in hardware, as GPUs with increasing processing power are currently available on the market at declining prices. The parallel computational scheme is well suited for cluster computing, leading to a good solution for segmenting very large data sets.

Global-scale object detection using satellite imagery

Abstract. In recent years, there has been a substantial increase in the availability of high-resolution commercial satellite imagery, enabling a variety of new remote-sensing applications. One of the main challenges for these applications is the accurate and efficient extraction of semantic information from satellite imagery. In this work, we investigate an important instance of this class of challenges which involves automatic detection of multiple objects in satellite images. We present a system for large-scale object training and detection, leveraging recent advances in feature representation and aggregation within the bag-of-words paradigm. Given the scale of the problem, one of the key challenges in learning object detectors is the acquisition and curation of labeled training data. We present a crowd-sourcing based framework that allows efficient acquisition of labeled training data, along with an iterative mechanism to overcome the label noise introduced by the crowd during the labeling process. To show the competence of the presented scheme, we show detection results over several object-classes using training data captured from close to 200 cities and tested over multiple geographic locations.

Neural technological approach to automated recognition of ground objects in images of space systems for remote sensing of the Earth

In this paper the ability to use neurocomputer technology to imaging remote sensing system are considered. The modeling of object classification is reviewed. The sequence and contents of the main stages of the construction of the neural network architecture are discussed. The spectral characteristics of different ground objects are used for classification and recognition of objects in satellite images. Comparative analysis of different types of neural networks in the classification of ground objects is given.

Simultaneous Fusion and Denoising of Panchromatic and Multispectral Satellite Images

To identify objects in satellite images, multispectral (MS) images with high spectral resolution and low spatial resolution, and panchromatic (Pan) images with high spatial resolution and low spectral resolution need to be fused. Several fusion methods such as the intensity–hue–saturation (IHS), the discrete wavelet transform, the discrete wavelet frame transform (DWFT), and the principal component analysis have been proposed in recent years to obtain images with both high spectral and spatial resolutions. In this paper, a hybrid fusion method for satellite images comprising both the IHS transform and the DWFT is proposed. This method tries to achieve the highest possible spectral and spatial resolutions with as small distortion in the fused image as possible. A comparison study between the proposed hybrid method and the traditional methods is presented in this paper. Different MS and Pan images from Landsat-5, Spot, Landsat-7, and IKONOS satellites are used in this comparison. The effect of noise on the proposed hybrid fusion method as well as the traditional fusion methods is studied. Experimental results show the superiority of the proposed hybrid method to the traditional methods. The results show also that a wavelet denoising step is required when fusion is performed at low signal-to-noise ratios.