Aircraft Detection Based on Shadow Affect in Optical Remotely Sensed Imagery

A new remote sensing image processing system's algorithm module has been introduced in this paper, which is coded with Visual C++ 6.0 program language and can process large volume of remote sensing image. At the same time, adopted key technologies in algorithm module are given. Two defects of American remote sensing image processing system called ERDAS has been put forward in image filter algorithm and the storage of pixel values that are out of data type range. In author's system two optimized methods has been implemented in these two aspects. By contrasted with ERDAS IMAGINE System, the two methods had been proved to be effective in image analysis.

Deep learning methods have been shown to significantly improve the performance of building extraction from optical remote sensing imagery. However, keeping the morphological characteristics, especially the boundaries, is still a challenge that requires further study. In this paper, we propose a novel fully convolutional network (FCN) for accurately extracting buildings, in which a boundary learning task is embedded to help maintain the boundaries of buildings. Specifically, in the training phase, our framework simultaneously learns the extraction of buildings and boundary detection and only outputs extraction results while testing. In addition, we introduce spatial variation fusion (SVF) to establish an association between the two tasks, thus coupling them and making them share the latent semantics and interact with each other. On the other hand, we utilize separable convolution with a larger kernel to enlarge the receptive fields while reducing the number of model parameters and adopt the convolutional block attention module (CBAM) to boost the network. The proposed framework was extensively evaluated on the WHU Building Dataset and the Inria Aerial Image Labeling Dataset. The experiments demonstrate that our method achieves state-of-the-art performance on building extraction. With the assistance of boundary learning, the boundary maintenance of buildings is ameliorated.

Nowadays, the increase of quality and resolution of remote sensing image will lead to the data's capacity extremely increase; A single computer, even a very expensive minicomputer or a PC server, is difficult to meet the disk space, memory space and processing time requirements when faced with this situation. Cloud computing can give us a new resolution to these problems. In this study, we proposed a Hadoop-based remote sensing image processing system (HBRSIPS), on the one hand, it re-defines a storage structure to adapt the characters of remote sensing images, on the other hand, We construct Java program classes based on MapReduce architecture, remote sensing image processing algorithms can be easily applied to the cloud computing environment. The experiments show, the proposed system can save remote sensing image into Hadoop's distribute storage environment and algorithms based on cloud environment were faster than single process algorithm.

This article made a detailed introduction to the design and implementation of image processing algorithm framework, with its focus on data-driven, image processing algorithm, UI and processing dialog optimization. Implemented by the C++ programming language, the final framework provides a new way and reference to the Customizable development of remote sensing image processing algorithm.The remote sensing image processing system is a set of technologies and methodoloies that can add enormous value in handling the problem of the earth sciences and related fields.Most practitioners are, primarily, professionals in fields such as hydrology, agriculture, engineering, forestry, geology and urban studies; giving them the writing remote sensing processing algorithm skills considerably enhances their expertise and capablities.

Object detection methods based on Convolutional Neural Networks (CNNs) require a large number of images with annotation information to train. In aircraft detection from remote sensing images (RSIs), aircraft targets are usually small and the cost of manual annotation is very high. In this article, we tackle the problem of weakly supervised aircraft detection from RSIs, which aims to learn detectors with only image-level annotations, i.e., without bounding-box labeled data during the training stage. Based on the fact that the feature maps learned from the CNN network are localizable, we propose a simple yet efficient aircraft detection algorithm called Weakly Supervised Learning in AlexNet (AlexNet-WSL). In AlexNet-WSL, we utilize the AlexNet CNN as backbone network, but replace the last two fully connected layers with a Global Average Pooling (GAP) and two convolutional layers. Based on the class activation maps, we generate heat maps via reverse weighting for locating the target object. Unlike object detection methods that require object location data for training, our proposal only needs image-level labelled data. We furthermore build a set of remote sensing aircraft images, the Weakly Supervised Aircraft Detection Dataset (WSADD) for algorithm benchmarking. The experimental results on the WSADD show that AlexNet-WSL effectively detects the aircraft and achieves a detection effect equivalent to the Faster R-CNN method and the YOLOv3 method, which both require bounding-box labelled data for training, with a lower false alarm rate and a shorter training time.

The articles in this special section focus on information extraction from high-spatial-resolution optical remotely sensed imagery technology and applications.

Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction.

This paper mainly studies the low-altitude UAV (unmanned aerial vehicle) remote sensing digital processing system. The system is mainly a set of digital remote sensing image processing system developed for the data acquired by the system. First, it describes the basic principles of photogrammetry on which the system is based, and mainly explains the advantages of the beam method area network adjustment. This article briefly describes the common methods of surveying technology for power transmission and transformation lines. UAV aerial survey technology and GIS platform are applied to electric power surveys; the UAV aerial survey data are used as the data source to establish a GIS database, and the detailed system design of the structure, interface, and database of the electric power survey geographic information system is carried out. The secondary development realizes the main functions of the system such as data management, spatial query, surface analysis, and spatial analysis, facilitating scientific information management and comprehensive analysis, providing auxiliary decision-making, and bringing new methods to electric power surveys. Secondly, this article points out that the system uses an ordinary digital camera with small image size and large distortion. This article is based on the basic principles of photogrammetry, and it is worthwhile to use navigation GPS. Thirdly, the accuracy of the aerial triangulation results of the system and the accuracy of the digital orthophoto were analyzed through a large amount of data.

Automatic targets recognition(ATR) of artificial objects in high resolution remote sensing images can be divided into two categories by the properties of targets. The first such building, a harbor which has fixed location and stable out looking. The other one, for example aircraft, whose location and posture is sensitive to the moment. Due to the variable sizes, colors, orientations, and complex background, aircraft detection is a difficult task in high resolution remote sensing images. In this paper, a simple and effective aircraft detection method with a single template is proposed, which exactly locates the object by outputting its geometric center, location and orientation. Compare to traditional method,this method only needs critical feature in the local areas of target and a binary template of aircraft. Compare to traditional Feature + Classifier method, it’s easy, simple and don’t need outline training,but also get high precision and low false rate in the same complicate background.

The increasing demand for Optical Remote Sensing Imagery (ORSI) has developed a critical area of research, particularly for detailed object recognition tasks such as infrastructure analysis, vehicle detection, and land cover assessment. Unlike traditional classification, fine-grained categorization involves distinguishing between visually similar subcategories that vary in size, orientation, and framework. This paper presents a comprehensive review of various Machine Learning (ML) as well as Deep Learning (DL) methods useful to this challenge. Traditional ML approaches such as ensemble methods, Decision Tree (DT), and Support Vector Machines (SVM) typically depend on handcrafted features and exhibit limited scalability when dealing with complex image representations. Conversely, DL approaches, including Deep Convolutional Neural Networks (DCNNs), attention-based models, and hybrid architectures, automatically learn hierarchical and discriminative features directly from raw image data, leading to significantly improved performance. The review also explores feature selection techniques aimed at enhancing classification accuracy. Finally, a comparative analysis of these methods is provided, highlighting the most effective approaches that evaluate future research and applications in fine-grained classification of RSI.

Motivated by the development of deep convolution neural networks (DCNNs), the aircraft detection from remote sensing images has gained tremendous progress. However, due to complex background and multi-scale characteristics, it remains a challenge in remote sensing detection. In this paper, we propose a two-stage aircraft detection method based on deep neural networks, which integrates Deconvolution operation with Position Attention mechanism (DPANet). Specifically, considering that remote sensing images are taken from the top-down perspective, which leads to significant external structural characteristic, we introduce a deconvolution module to capture the external structural feature representation of aircraft during the feature map generation process. Moreover, aiming at reducing the error detections caused by complex background in remote sensing, we propose a position attention module in the second stage. By calculating the feature similarity between any two pixels of the target feature map, DPANet can extract the complicated internal structure feature representation of aircraft, which improve the ability to distinguish background and aircraft. By integrating the deconvolution and position attention modules, DPANet can provide better representation ability for the structural characteristic of aircraft in remote sensing images. Experimental results show that the proposed method can effectively reduce the error detections and improve the accuracy of the aircraft detection.

Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks.

Corn is an important food crop worldwide, and its yield is directly related to Chinese food security. Accurate remote sensing extraction of corn can realize the rational application of land resources, which is of great significance to the sustainable development of modern agriculture. In the field of large-scale crop remote sensing classification, single-period optical remote sensing images often cannot achieve high-precision classification. To improve classification accuracy, multiple time series image combinations have gradually been adopted. However, due to the influence of cloudy and rainy weather, it is often difficult to obtain complete time series optical images. Synthetic aperture radar (SAR) data are imaged by microwaves, which have strong penetrating power and are not affected by clouds. A critical way to solve this problem is to use SAR images to compensate for the lack of optical images and obtain a complete time series image in the corn-growing season. However, SAR images have limited wavelengths and cannot provide important wavelengths, such as visible light bands and near-infrared information. To solve this problem, this study took Zhaodong City, a vital corn-planting base in China, as the research area; took GF-6/GF-3 and Sentinel-1/Sentinel-2 as remote sensing data sources; designed12 classification scenarios; analyzed the best classification period and the best time series combination of corn classification; studied the influence of SAR images on the classification results of time series images; and compared the classification differences between GF-6/GF-3 and Sentinel-1/Sentinel-2. The results show that the classification accuracy of time series combinations is much higher than that of single-period images. The polarization characteristics of SAR images can improve the classification accuracy with time series images. The classification accuracy of GF series images from China is obviously higher than that of Sentinel series images. The research performed in this paper can provide a reference for agricultural classification by using remote sensing data.

Although deep learning techniques have achieved noticeable success in aircraft detection, the scale heterogeneity, position difference, complex background interference, and speckle noise keep aircraft detection in large-scale synthetic aperture radar (SAR) images challenging. To solve these problems, we propose the geospatial transformer framework and implement it as a three-step target detection neural network, namely, the image decomposition, the multiscale geospatial contextual attention network (MGCAN), and result recomposition. First, the given large-scale SAR image is decomposed into slices via sliding windows according to the image characteristics of the aircraft. Second, slices are input into the MGCAN network for feature extraction, and the cluster distance nonmaximum suppression (CD-NMS) is utilized to determine the bounding boxes of aircraft. Finally, the detection results are produced via recomposition. Two innovative geospatial attention modules are proposed within MGCAN, namely, the efficient pyramid convolution attention fusion (EPCAF) module and the parallel residual spatial attention (PRSA) module, to extract multiscale features of the aircraft and suppress background noise. In the experiment, four large-scale SAR images with 1-m resolution from the Gaofen-3 system are tested, which are not included in the dataset. The results indicate that the detection performance of our geospatial transformer is better than Faster R-CNN, SSD, Efficientdet-D0, and YOLOV5s. The geospatial transformer integrates deep learning with SAR target characteristics to fully capture the multiscale contextual information and geospatial information of aircraft, effectively reduces complex background interference, and tackles the position difference of targets. It greatly improves the detection performance of aircraft and offers an effective approach to merge SAR domain knowledge with deep learning techniques.

Optical remote sensing (RS) satellites perform imaging in the visible and infrared electromagnetic spectrum to collect data and analyze information on the optical characteristics of the objects of interest. However, optical RS is sensitive to illumination and atmospheric conditions, especially clouds, and multiple acquisitions are typically required to obtain an image of sufficient quality. To accurately reproduce surface information that has been contaminated by clouds, this work proposes a generative adversarial network (GAN)-based cloud removal framework using a distortion coding network combined with compound loss functions (DC-GAN-CL). A novel generator embedded with distortion coding and feature refinement mechanisms is applied to focus on cloudy regions and enhance the transmission of optical information. In addition, to achieve feature and pixel consistency, both coherent semantics and local adaptive reconstruction factors are considered in our loss functions. Extensive numerical evaluations on RICE1, RICE2, and Paris datasets are performed to validate the good performance achieved by the proposed DC-GAN-CL in both peak signal-to-noise ratio (PSNR) and visual perception. This system can thus restore images to obtain similar quality to cloud-free reference images, in a dynamic range of over 30 dB. The restoration effect on the coherence of image semantics produced by this technique is competitive compared with other methods.