This letter presents a novel change detection method for multitemporal synthetic aperture radar images based on PCANet. This method exploits representative neighborhood features from each pixel using ...PCA filters as convolutional filters. Thus, the proposed method is more robust to the speckle noise and can generate change maps with less noise spots. Given two multitemporal images, Gabor wavelets and fuzzy c-means are utilized to select interested pixels that have high probability of being changed or unchanged. Then, new image patches centered at interested pixels are generated and a PCANet model is trained using these patches. Finally, pixels in the multitemporal images are classified by the trained PCANet model. The PCANet classification result and the preclassification result are combined to form the final change map. The experimental results obtained on three real SAR image data sets confirm the effectiveness of the proposed method.
Automatic ship detection from optical satellite imagery is a challenging task due to cluttered scenes and variability in ship sizes. This letter proposes a detection algorithm based on saliency ...segmentation and the local binary pattern (LBP) descriptor combined with ship structure. First, we present a novel saliency segmentation framework with flexible integration of multiple visual cues to extract candidate regions from different sea surfaces. Then, simple shape analysis is adopted to eliminate obviously false targets. Finally, a structure-LBP feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Experimental results on numerous panchromatic satellite images validate that our proposed scheme outperforms other state-of-the-art methods in terms of both detection time and detection accuracy.
Automatic ship detection in high-resolution optical satellite images with various sea surfaces is a challenging task. In this letter, we propose a novel detection method based on sea surface analysis ...to solve this problem. The proposed method first analyzes whether the sea surface is homogeneous or not by using two new features. Then, a novel linear function combining pixel and region characteristics is employed to select ship candidates. Finally, Compactness and Length-width ratio are adopted to remove false alarms. Specifically, based on the sea surface analysis, the proposed method cannot only efficiently block out no-candidate regions to reduce computational time, but also automatically assign weights for candidate selection function to optimize the detection performance. Experimental results on real panchromatic satellite images demonstrate the detection accuracy and computational efficiency of the proposed method.
Due to the difference of "mean information" between substitution component and substituted component, spectral distortion often occurs in component substitution (CS) pansharpening. In this paper, a ...data fitting scheme is adopted to improve spectral quality in image fusion based on well-established CS approach. A generalized CS framework that is capable of modeling any CS image fusion method is also presented. In this framework, instead of injecting detail information of panchromatic (Pan) image into substituted component, the data fitting strategy is designed to adjust the mean information of Pan image in the construction of substitution component. The data fitting scheme involves two matrix subtractions and one matrix convolution. It is fast in implementation and is effective to avoid the spectral distortion problem. Experimental results on a large number of Pan and multispectral images show that the improved CS methods have good performance on the spatial and spectral fidelity. Moreover, experiments carried out on large-size images also show an excellent running time performance of the proposed methods.
Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great ...opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.
Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop’s growth and implement ...effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea–land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.
It has been observed that the existing convolutional neural network (CNN)-based ship detection models often result in high false detection rate in low-resolution optical satellite images. This ...problem arises from the following factors: 1) the current 8-b rescaling schemes make the images lose some important information about ships in low-resolution imagery; 2) the effective features of ships at low resolution are far fewer than those of ships at high resolution; and 3) the detection of low-resolution ships is more sensitive to object-background contrast variation. To solve these problems, a low-resolution marine object (LMO) detection YOLO model, called LMO-YOLO, is proposed in this article. First, a multiple linear rescaling scheme is developed to quantize the original satellite images into 8-b images; second, dilated convolutions are included in a YOLO network to extract object features and object-background features; finally, an adaptive weighting scheme is designed to balance the loss between easy-to-detect ships and hard-to-detect ships. The proposed method was validated by level 1 product images captured by the wide-field-of-view sensor on the GaoFen-1 satellite. The experimental results demonstrated that our method accurately detected ships from low-resolution images and outperformed state-of-the-art methods.
Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, ...we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.
Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a ...concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness.
We describe here the direct connection between the molecular conformation of a conjugated macrocycle and its macroscopic charge transport properties. We incorporate chiral, helical perylene diimide ...ribbons into the two separate macrocycles as the n-type, electron transporting material. As the macrocycles’ films and electronic structures are analogous, the important finding is that the macrocycles’ molecular structures and their associated dynamics determine device performance in organic field effect transistors. We show the more flexible macrocycle has a 4-fold increase in electron mobility in field effect transistor devices. Using a combination of spectroscopy and density functional theory calculations, we find that the origin of the difference in device performance is the ability of more flexible isomer to make intermolecular contacts relative to the more rigid counterpart.
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