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The usage of remote sensing to observe environments necessitates interdisciplinary approaches to derive effective, impactful research. One remote sensing technique, Synthetic Aperture Radar, has shown significant benefits over traditional remote sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning...
Change detection (CD) for multitemporal hyperspectral images (HSI) consists of two steps, change feature extraction and identification. This paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSD_SS), to extract clean change features from corrupted spectral change vectors (SCV) of multitemporal HSI. It decomposes SCV into spatially smoothed low-rank data,...
In this paper, supervised contractive autoencoders (SCAEs) combined with fuzzy c-means (FCM) clustering are developed for change detection of synthetic aperture radar (SAR) images, which aim to take advantage of deep neural networks to capture changed features. Given two original SAR images, Lee filter is used in preprocessing and the difference image (DI) is obtained by log ratio method. Then, FCM...
This paper presents a novel unsupervised superpixel-based change detection approach to detect multiple changes in Very-High-Resolution remote sensing images. The proposed approach investigates the spectral-spatial variations at superpixel level which aims to enhance the traditional pixel level change detection performance. In particular, superpixel representation of the spectral change vectors is...
As we have entered an era of information, the RS data are undergoing a plosive growth. The needs of large-scale earth observation have led to the development of high-resolution and high-dimensionality RS data, which has posed significant challenges in processing and application. In this paper, we demonstrate some possible solution of large-scale domestic hyperspectral data processing and geological...
SAR ship detection is essential to marine monitoring. Recently, with the development of the deep neural network and the spring of the SAR images, SAR ship detection based on deep neural network has been a trend. However, the multi-scale ships in SAR images cause the undesirable differences of features, which decrease the accuracy of ship detection based on deep learning methods. Aiming at this problem,...
Multi-temporal PolSAR data is suitable for crops classification and growth monitoring. It is still difficult to establish a classifier with good robustness and high generation over a long temporal acquisition duration. This work aims to provide a solution to this task by exploring benefits from both the target scattering mechanism interpretation and the advanced deep learning. A polarimetric-feature-driven...
Faster Region based convolutional neural networks (FRCN) has shown great success in object detection in recent years. However, its performance will degrade on densely packed objects in real remote sensing applications. To address this problem, an enhanced deep CNN based method is developed in this paper. Following the common pipeline of “CNN feature extraction + region proposal + Region classification”,...
Classification in remote sensing, similar to semantic segmentation in computer vision, is aimed to assign a label to each pixel in images to indicate which class it belongs to. Fully convolutional networks (FCN), one of semantic segmentation methods, is proposed to tackle this problem in fully PolSAR images in this paper. To exploit the polarimetric information in PolSAR images, H-A-α polarimetric...
This paper presents a novel unsupervised image classification method for polarimetric synthetic aperture radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, we design an energy function for unsupervised PolSAR image classification...
The new advanced very high resolution (VHR) synthetic aperture radar (SAR) sensor is a kind of high-tech imaging radar developed rapidly in recent years, and it can get even less than 1 m high resolution SAR image. The feature of the VHR SAR image is different from the low or medium resolution SAR image and it contains more abundant information, so the traditional SAR image classification methods...
Monitoring urban spatial information is important to hold the process of urbanization for keeping balance between the human activity and the environment. To promote the application extent of the remote sensing technology in the topic of ghost cities, an effective method was proposed to monitor and evaluate “ghost city” phenomenon in the prefecture level city of China by taking advantage of multi-source...
In this letter, a novel deep-leaming-based feature selection method based on Poisson Gamma Belief Network (PGBN), is proposed to extract multi-layer feature from SAR images data. As a deep Bayesian generative network, PGBN has the ability to extract a multilayer structured representation from the complex SAR images owing to the existence of Poisson likelihood and multilayer gamma hidden variables,...
Water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. Remote sensing has increasingly been used for water mapping in rural areas; especially, hyperspectral remote sensing image characterized with rich spectrum information provide greater potential for high-accuracy land cover classiflcation, however, the hundreds...
We proposed a deep convolutional network for multi-spectral image pan-sharpening to overcome the drawbacks of traditional methods and improve the fusion accuracy. To break the performance limitation of deep networks, residual learning with specific adaption to image fusion tasks is applied to optimize the architecture of proposed network. Results of adequate experiments support that our model can...
This paper aims at the feature enhancement for multi-polarimetric synthetic aperture radar (SAR) images. A novel approach based on PDE and regularization which is an extension of the original PDE and regularization methods is proposed. It contains the PDE term for speckle suppression and the sparsity constraint term for strong scatter enhancement. The PDE term is established by combining the ROA detected...
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