Multiresolution Remote Sensing Images Research Articles

Long-term Land Cover Change Detection Using Multisensor and Multiresolution Remote Sensing Images: A Case Study of Chang’an University, China

Long-term Land Cover Change Detection Using Multisensor and Multiresolution Remote Sensing Images: A Case Study of Chang’an University, China

A Novel Adaptive Hybrid Fusion Network for Multiresolution Remote Sensing Images Classification

With the rapid development of earth observation technology, panchromatic (PAN) and multispectral (MS) images have also become easier to obtain. The multiresolution classification of PAN and MS images as a basic MS image analysis task has become a research hotspot. The main challenge in this field is how to process data and extract features to improve classification accuracy effectively. In this article, we design a novel adaptive hybrid fusion network (AHF-Net) for multiresolution remote sensing image classification. It includes two parts: data fusion and feature fusion. In the data fusion part, we propose an adaptive weighted intensity-hue-saturation (AWIHS) strategy, which can reduce the difference between MS and PAN images by adaptively adding each other’s unique information from the perspective of information sharing. In the feature fusion part, starting from the second-order correlation of features, we propose a correlation-based attention feature fusion (CAFF) module. It can improve the discrimination of fusion features by adaptively determining the fusion coefficient according to the importance of the input feature channel. Based on AWIHS and CAFF, inspired by the idea of feature pyramid, we combine the multilevel feature fusion and the dual-branch residual network as the backbone network of AHF-Net. By combining AWIHS and CAFF modules with the backbone network, our AHF-Net can effectively improve the classification accuracy of multiresolution remote sensing images. The effectiveness of the proposed algorithm has been verified on multiple data sets. Our code and model are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/1826133674/AHF-Net</uri> .

Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model

The registration of multiresolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching. However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images. In this study, we propose a framework for generating deep features via a deep residual encoder (DRE) fused with shallow features for multiresolution remote sensing image registration. Through an L2 normalization Siamese network (L2-Siamese) based on the DRE, the multiscale loss function is used to learn the attribute characteristics and distance characteristics of two key points and obtain the trained feature extractor. Finally, the DRE is used to extract the deep features of the key points and their neighbors, which are concatenated with the shallow features into a fusion feature vector to complete the image registration. We performed comprehensive experiments on four sets of multiresolution optical remote sensing images and two sets of synthetic aperture radar images. The results demonstrate that the proposed registration model can achieve subpixel registration. The relative registration accuracy improved by 1.6%–7.5%, whereas the overall performance improved by 4.5%–14.1%.

Key Frame Extraction of Multi-Resolution Remote Sensing Images Under Quality Constraint

Abstract In key frame extraction of multi-resolution remote sensing image using traditional key frame image feature extraction method, only the feature information of remote sensing images, rather than cluster operation of the remote sensing images is considered, which leads to low efficiency and poor quality of extraction results. To this end, the key frame extraction algorithm of multi-resolution remote sensing image under quality constraint was proposed. Through similarity between image features and the selected image frame, rough key frame can be extracted. On this basis, the key frame extraction of multi resolution remote sensing image based on quality constraints was used to perform clustering operation for multi-resolution remote sensing image corresponding to rough key frame, which shortened the time length for retrieval of key frame image. According to the clustering results, multi-resolution remote sensing images were divided into several clusters. The key frame of each cluster can be obtained by calculating the distance between remote sensing image and cluster center. For key frames that had been determined, their quality was evaluated to meet standard, so as to realize effective extraction of key frame of multi-resolution remote sensing images. The experimental results show that the proposed method can significantly improve the quality of key frame extraction of multi-resolution remote sensing images.

A multiscale subpixel mixture analysis applied for melt detection using passive microwave and radar scatterometer image time series of the Antarctic Peninsula (1999–2009)

ABSTRACTThis paper reports a comparative analysis performed on a fraction-image time series of the Antarctic Peninsula from the period 1999–2009 generated by multiresolution remote-sensing images (SSM/I and SSMI/S with 25 km and QuikSCAT with 2.225 km spatial resolutions) for snow-melt detection. Our method is based on the (a) preprocessing of multitemporal remote-sensing data, (b) subpixel mixture analysis of SSMI and QuikSCAT image time series, and (c) evaluation of subpixel analysis, including an assessment of fraction images of wet snow using an independent ASAR dataset and sensitivity analysis on the melt metrics measured by these images. The temporal dynamics of the melt indices derived from the wet-snow fraction images presented a more realistic pattern than the traditional melt metrics measured by Boolean snow-melt detection approaches. Because the snow melt actually occurs at the pixel fractions, the multiscale analysis that was performed suggests an overestimation of the melt metrics calculated using Boolean approaches (which assume that the entire area of the detected pixel shows snow melt). The melt metrics measurements show an overestimation according to the decrease in spatial resolution related to the multiplicative effect of a larger pixel area.

Classification of Multisensor and Multiresolution Remote Sensing Images Through Hierarchical Markov Random Fields

This letter proposes two methods for the supervised classification of multisensor optical and synthetic aperture radar images with possibly different spatial resolutions. Both the methods are formulated within a unique framework based on hierarchical Markov random fields. Distinct quad-trees associated with the individual information sources are defined to jointly address multisensor, multiresolution, and possibly multifrequency fusion, and are integrated with finite mixture models and the marginal posterior mode criterion. Experimental validation is conducted with Pleiades, COSMO-SkyMed, RADARSAT-2, and GeoEye-1 data.

Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images

A new technique for the segmentation of single- and multiresolution (MR) remote sensing images is proposed. To guarantee the preservation of details at fine scales, edge-based watershed is used, with automatically generated markers that help in limiting oversegmentation. For MR images, the panchromatic and multispectral components are processed independently, extracting both the edge maps and the morphological and spectral markers that are eventually fused at the highest resolution, thus avoiding any information loss induced by pansharpening. Numerical results on object layer extraction and simple classification tasks prove the proposed techniques to provide accurate segmentation maps, which preserve fine details and, contrary to state-of-the-art products, can single out objects equally well at very different scales.

Supervised Classification of Multisensor and Multiresolution Remote Sensing Images With a Hierarchical Copula-Based Approach

In this paper, we develop a novel classification approach for multiresolution, multisensor [optical and synthetic aperture radar (SAR)], and/or multiband images. This challenging image processing problem is of great importance for various remote sensing monitoring applications and has been scarcely addressed so far. To deal with this classification problem, we propose a two-step explicit statistical model. We first design a model for the multivariate joint class-conditional statistics of the coregistered input images at each resolution by resorting to multivariate copulas. Such copulas combine the class-conditional marginal probability density functions (pdfs) of each input channel that are estimated by finite mixtures of well-chosen parametric families. We consider different distribution families for the most common types of remote sensing imagery acquired by optical and SAR sensors. We then plug the estimated joint pdfs into a hierarchical Markovian model based on a quad-tree structure, where each tree-scale corresponds to the different input image resolutions and to corresponding multiscale decimated wavelet transforms, thus preventing a strong resampling of the initial images. To obtain the classification map, we resort to an exact estimator of the marginal posterior mode. We integrate a prior update in this model in order to improve the robustness of the developed classifier against noise and speckle. The resulting classification performance is illustrated on several remote sensing multiresolution data sets, including very high resolution and multisensor images acquired by COSMO-SkyMed and GeoEye-1.

Hierarchical Texture-Based Segmentation of Multiresolution Remote-Sensing Images

In this paper, we propose a new algorithm for the segmentation of multiresolution remote-sensing images, which fits into the general split-and-merge paradigm. The splitting phase singles out clusters of connected regions that share the same spatial and spectral characteristics. These clusters are then regarded as atomic elements of more complex structures, particularly textures, that are gradually retrieved during the merging phase. The whole process is based on a recently developed hierarchical model of the image, which accurately describes its textural properties. In order to reduce the computational burden and preserve contours at the highest spatial definition, the algorithm works on the high-resolution panchromatic data first, using low-resolution full spectral information only at a later stage to refine the segmentation. It is completely unsupervised, with just a few parameters set at the beginning, and its final product is not a single segmentation map but rather a sequence of nested maps which provide a hierarchical description of the image, at various scales of observations. The first experimental results, obtained on a remote-sensing Ikonos image, are very encouraging and confirm the algorithm potential.