Parallel Classification and Detection System for High-Resolution Remote Sensing Images

Content-based remote sensing (RS) image retrieval (CBRSIR) is a critical way to organize high-resolution RS (HRRS) images in the current big data era. The increasing volume of HRRS images from different satellites and sensors leads to more attention to the cross-source CSRSIR (CS-CBRSIR) problem. Due to the data drift, one crucial problem in CS-CBRSIR is the modality discrepancy. Most existing methods focus on finding a common feature space for various HRRS images to address this issue. In this space, their similarity relations can be measured directly to obtain the cross-source retrieval results straight. This way is feasible and reasonable, however, the specific information corresponding to HRRS images from different sources is always ignored, limiting retrieval performance. To overcome this limitation, we develop a new model for CS-CBRSIR in this paper named dual modality collaborative learning (DMCL). To fully explore the specific information from diverse HRRS images, DMCL first introduces ResNet50 as the feature extractor. Then, a common space mutual learning module is developed to map the specific features into a common space. Here, the modality discrepancy is reduced from the aspects of features and their distributions. Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. The comprehensive experiments are conducted on a public dataset. Compared with many existing methods, the behavior of our DMCL is stronger. These encouraging results for a public dataset indicate that the proposed DMCL is useful in CS-CBRSIR tasks.

Semantic segmentation is a fundamental task in remote sensing image understanding. Recently, Deep Convolutional Neural Networks (DCNNs) have considerably improved the performance of the semantic segmentation of natural scenes. However, it is still challenging for very high-resolution remote sensing images. Due to the large and complex scenes as well as the influence of illumination and imaging angle, it is particularly difficult for the existing methods to accurately obtain the category of pixels at object boundaries—the so-called boundary blur. We propose a framework called Boundary-Aware Semi-Supervised Semantic Segmentation Network (BAS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> Net), which obtains more accurate segmentation results without additional annotation workload, especially at the object boundaries. The Channel-weighted Multi-scale Feature (CMF) module balances semantic and spatial information, and the Boundary Attention Module (BAM) weights the features with rich semantic boundary information to alleviate the boundary blur. Additionally, to decrease the amount of difficult and tedious manual labeling of remote sensing images, a discriminator network infers pseudolabels from unlabeled images to assist semisupervised learning, and further improves the performance of the segmentation network. To validate the effectiveness of the proposed framework, extensive experiments have been performed on both the ISPRS Vaihingen dataset, and the novel remote sensing dataset AIR-SEG with more categories, and complex boundaries. The results demonstrate a significant improvement of accuracy especially on boundaries and for small objects.

The presence of shadows in high-resolution (HR) remote-sensing images reduces object detection accuracy. To address this problem, in this paper, we proposed a deep neural network algorithm for shadow detection by using the AISD and SSAD remote-sensing shadow image datasets. To improve the ability to extract spatial information from feature maps, we developed a cross-spatial attention module that focuses on semantic information in the horizontal and vertical directions at each position point on the remote-sensing image. This module overcomes the limitations of existing technologies in accurately judging small areas and suspected shadow areas and in missing or incorrectly detected shadow areas. In addition, to improve the ability to extract shadow features and the accuracy of shadow detection in remote-sensing images, we developed a channel attention module that assigns more attention to channels that conform to the shadow color characteristics. The network architecture comprises an encoder – decoder structure, with ResNeXt50 used as the backbone for the encoder and a multi resolution parallel fusion (MRPF) designed for the decoder; cross-spatial and channel attention were incorporated into the decoder unit. Experimental results demonstrated the superior performance of the proposed algorithm, with an F1 score of 92.6% for the shadow category on the test set, thus, outperforming other algorithms and making the proposed method an effective solution for shadow detection in HR remote-sensing images.

This paper presents a parallel face detection system based on multi-core processors, of which the hardware part uses the ZYNQ chips and multi-core processors and the software part includes the input and output of the image data and parallel detection. The part of parallel detection uses the cascade classifier based on MB-LBP feature and Gentle Adaboost algorithm. This system is aimed for a low-power multicore processor that is well suited to embedded system. The experimental results indicate that the parallel face detection system reduces the running time of the algorithm comparing with that on the ARM platform. Moreover, our system has outstanding performance and very high accuracy.

The condition along the high-speed railway is very important for the safe operation. In the paper, man-made objects along the high-speed railway are used as detecting targets. The paper analyses the existing forms of hidden dangers which are caused by man-made objects along the high-speed railway and what harms they could bring to operation of the high-speed railway. The method of detecting man-made objects along the high-speed railway based on high resolution remote sensing images is studied using remote sensing image classification. According to the established method which is suitable for detecting man-made objects along the high-speed railway, firstly detecting and extracting man-made objects along the high-speed railway, and improving classification accuracy by GIS vector data. Then, using the method to detect man-made objects along the Xuzhou section of Beijing-Shanghai high-speed railway based on GF-3. The results show that there is hidden danger of the plastic house, which proves the effectiveness and practical significance of the method.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

Target classification and recognition (TCR) are important information-extraction techniques for high-resolution remote sensing images (HRIs). However, because methods with high accuracy usually have higher time complexity, the massive remote sensing image has brought great difficulties for realtime application. In this article, we propose a hybrid, heterogeneous parallel processing algorithm to improve the efficiency TCR based on the Crossmodel Neural Cognitive Computing (CNCC) algorithm [parallel TCR based on MapReduce of hierarchical CNCC (PTCR-C)]. Parallel programming technologies used in this article include Open Multiprocessing (OpenMP), Compute Unified Device Architecture (CUDA), and the message passing interface (MPI). Experiments on public remote sensing image data sets show that the PTCR-C algorithm can effectively improve the efficiency of the original TCR algorithm. The PTCR-C algorithm has better augmented ability and provides the reference for further real-time TCR of remote sensing applications.

Remote Sensing (RS) image classification has recently attracted great attention for its application in different tasks, including environmental monitoring, battlefield surveillance, and geospatial object detection. The best practices for these tasks often involve transfer learning from pre-trained Convolutional Neural Networks (CNNs). A common approach in the literature is employing CNNs for feature extraction, and subsequently train classifiers exploiting such features. In this paper, we propose the adoption of transfer learning by fine-tuning pre-trained CNNs for end-to-end aerial image classification. Our approach performs feature extraction from the fine-tuned neural networks and remote sensing image classification with a Support Vector Machine (SVM) model with linear and Radial Basis Function (RBF) kernels. To tune the learning rate hyperparameter, we employ a linear decay learning rate scheduler as well as cyclical learning rates. Moreover, in order to mitigate the overfitting problem of pre-trained models, we apply label smoothing regularization. For the fine-tuning and feature extraction process, we adopt the Inception-v3 and Xception inception-based CNNs, as well the residual-based networks ResNet50 and DenseNet121. We present extensive experiments on two real-world remote sensing image datasets: AID and NWPU-RESISC45. The results show that the proposed method exhibits classification accuracy of up to 98%, outperforming other state-of-the-art methods.

Land cover classification has achieved significant advances by employing deep convolutional network (ConvNet) based methods. Following the paradigm of learning deep models, land cover classification is modeled as semantic segmentation of very high resolution remote sensing images. In order to obtain accurate segmentation results, high-level categorical semantics and low-level spatial details should be effectively fused. To this end, we propose a novel bidirectional gird fusion network to aggregate the multilevel features across the ConvNet. Specifically, the proposed model is characterized by a bidirectional fusion architecture, which enriches diversity of feature interaction by encouraging bidirectional information flow. In this way, our model gains mutual benefits between top-down and bottom-up information flows. Moreover, a grid fusion architecture is then followed for further feature refinement in a dense and hierarchical fusion manner. Finally, effective feature upsampling is also critical for the multiple fusion operations. Consequently, a content-aware feature upsampling kernel is incorporated for further improvement. Our whole model consistently achieves significant improvement over state-of-the-art methods on two major datasets, ISPRS and GID.

Despite the effectiveness in extracting information from images, the existing neural network model is not suitable for extracting cultivated land information from high resolution remote sensing images (HRRSIs). In this paper, three effective improvements are made to the existing Deeplab model, i.e., (1) A parameter to adjust dilated convolution kernel is introduced, (2) A linear activation function is selected, and (3) a more precise decoder group is added to the Deeplab model structure. With the above improvements, a HRI-Deeplab (High Resolution Image) model for extracting cultivated land information from HRRSIs is proposed. The HRI-Deeplab model can accurately extract the cultivated land and the advantage is to extract different objects with similar features. 10 large-scale remote sensing images of Feicheng city, China from 2016 to 2017 were selected for experiment. Through the above large-scale remote sensing images, we have cut and produced about 13,000 training images and 17,000 test images. The green land and bare land of cultivated land were selected as the object of model extraction. The results show that the extraction accuracy, recall rate and Kappa coefficients of the HRI-Deeplab are 90%, 89% and 91% respectively, which are obviously better than those of the Deeplab model and SegNet model.

Given a pair of bitemporal very high resolution (VHR) remote sensing images, the semantic change detection task aims to locate land surface changes and identify their semantic classes. The existing algorithms use independent branches to locate and identify separately without considering the association between branches. In this article, we propose an end-to-end spatially and semantically enhanced Siamese network (SSESN) for semantic change detection. The SSESN aggregates the rich spatial and semantic information in the VHR image through a designed spatial and semantic feature aggregation module. Additionally, a change-aware module is proposed to decouple the aggregated features. Features in the binary branch are fused to the semantic branches as prior location information. This allows the spatially enhanced features to predict changed regions and the semantically enhanced features to refine the region categorizations. Experimental results show that our method provides comparable results with the state-of-the-art binary change detection and semantic change detection algorithms.

To improve the classification results of high-resolution remote sensing images (RSIs), it is necessary to use feature transfer methods to mine the relevant information between high-resolution RSIs and low-resolution RSIs to train the classifiers together. Most of the existing feature transfer methods can only handle homogeneous data (i.e., data with the same dimension) and are susceptible to the quality of the RSIs, while RSIs with different resolutions present different feature dimensions and samples obtained from illumination conditions. To obtain effective classification results, unlike existing methods that focus only on the projection transformation in feature space, a joint feature-space and sample-space heterogeneous feature transfer (JFSSS-HFT) method is proposed to simultaneously process heterogeneous multi-resolution images in feature space using projection matrices of different dimensions and reduce the impact of outliers by adaptive weight factors in the sample space simultaneously to reduce the occurrence of negative transfer. Moreover, the maximum interclass variance term is embedded to improve the discriminant ability of the transferred features. To solve the optimization problem of JFSSS-HFT, the alternating-direction method of multipliers (ADMM) is introduced to alternatively optimize the parameters of JFSSS-HFT. Using different types of ship patches and airplane patches with different resolutions, the experimental results show that the proposed JFSSS-HFT obtains better classification results than the typical feature transferred methods.

Building extraction from very high-resolution remote sensing images is a fundamental task and is widely used in applications such as change detection, disaster assessment, and real-time update of geographic information databases. However, due to the complexity of the geographical environment and the diversity of target features, accurate automatic building extraction remains very challenging. With the fast development of deep learning techniques, convolutional neural networks(CNN) have been widely used in remote sensing research and have achieved considerable results. But for large urban area-based building detection tasks, the CNN-based method usually gets into local optima and generates many false positive detections around building boundaries. To avoid the local optima and be aware of non-local information, this paper proposes a hybrid feature extraction model based on the combination of the CNN and Transformer to realize the automatic building detection from very high-resolution remote sensing images. Meanwhile, a multi-constraint weighting mechanism is proposed to enhance the ability of the model to recognize the regular geometric boundaries of buildings. Comprehensive experiments are conducted on the three different datasets. The proposed MC-TRANSU achieves the best F1-score, IoU, compared to the state-of-the-art methods such as SegNet, TransUnet, Swin-Unet, the detection accuracy improved around 5%. Quantitative and qualitative results verify the superiority and effectiveness of our model.

Based on analysis of roads features in high resolution remote sensing (RS) images, we improve the traditional algorithm for automatic edge detection and extraction and put forward a new methodology. Mathematical morphology reconstruction and boundary tracing algorithm are introduced to automatically detect and extract edges from high resolution RS images. Three factors, P factor, F factor and C factor, are defined to describe the shape characteristics of edges. K-mean cluster algorithm is then utilized to identify and extract road edges from the huge edge sets. Finally an example gives fine result of this methodology.

We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.