scholarly journals Disparity Estimation of High-Resolution Remote Sensing Images with Dual-Scale Matching Network

2021 ◽  
Vol 13 (24) ◽  
pp. 5050
Author(s):  
Sheng He ◽  
Ruqin Zhou ◽  
Shenhong Li ◽  
San Jiang ◽  
Wanshou Jiang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Stereo Matching ◽  
High Efficiency ◽  
Disparity Estimation ◽  
Coarse Grained ◽  
Stereo Images ◽  
New Paradigm ◽  
Fine Grained ◽  
Intractable Problems

As an essential task in remote sensing, disparity estimation of high-resolution stereo images is still confronted with intractable problems due to extremely complex scenes and dynamically changing disparities. Especially in areas containing texture-less regions, repetitive patterns, disparity discontinuities, and occlusions, stereo matching is difficult. Recently, convolutional neural networks have provided a new paradigm for disparity estimation, but it is difficult for current models to consider both accuracy and speed. This paper proposes a novel end-to-end network to overcome the aforementioned obstacles. The proposed network learns stereo matching at dual scales, in which the low one captures coarse-grained information while the high one captures fine-grained information, helpful for matching structures of different scales. Moreiver, we construct cost volumes from negative to positive values to make the network work well for both negative and nonnegative disparities since the disparity varies dramatically in remote sensing stereo images. A 3D encoder-decoder module formed by factorized 3D convolutions is introduced to adaptively learn cost aggregation, which is of high efficiency and able to alleviate the edge-fattening issue at disparity discontinuities and approximate the matching of occlusions. Besides, we use a refinement module that brings in shallow features as guidance to attain high-quality full-resolution disparity maps. The proposed network is compared with several typical models. Experimental results on a challenging dataset demonstrate that our network shows powerful learning and generalization abilities. It achieves convincing performance on both accuracy and efficiency, and improvements of stereo matching in these challenging areas are noteworthy.

scholarly journals An Edge-Sense Bidirectional Pyramid Network for Stereo Matching of VHR Remote Sensing Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4025
Author(s):  
Rongshu Tao ◽  
Yuming Xiang ◽  
Hongjian You
Keyword(s):  
Remote Sensing ◽  
Stereo Matching ◽  
Tall Buildings ◽  
Disparity Estimation ◽  
Complex Structures ◽  
Learning Networks ◽  
Remote Sensing Images ◽  
Essential Step ◽  
Disparity Range ◽  
The Cost

As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.

scholarly journals Disparity estimation using Graph cuts for road applications

2021 ◽  
Vol 297 ◽  
pp. 01055
Author(s):  
Mohamed El Ansari ◽  
Ilyas El Jaafari ◽  
Lahcen Koutti
Keyword(s):  
Stereo Matching ◽  
Optimal Solution ◽  
Similarity Criterion ◽  
Graph Cuts ◽  
Search Space ◽  
Disparity Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Current Frame ◽  
Edge Points

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.

scholarly journals Generalized Zero-Shot Vehicle Detection in Remote Sensing Imagery via Coarse-to-Fine Framework

2019 ◽  
Author(s):  
Hong Chen ◽  
Yongtan Luo ◽  
Liujuan Cao ◽  
Baochang Zhang ◽  
Guodong Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vehicle Detection ◽  
Training Data ◽  
Coarse Grained ◽  
Vehicle Classification ◽  
Vehicle Localization ◽  
Fine Grained ◽  
Coarse To Fine ◽  
Generalized Zero ◽  
Detection And Recognition

Vehicle detection and recognition in remote sensing images are challenging, especially when only limited training data are available to accommodate various target categories. In this paper, we introduce a novel coarse-to-fine framework, which decomposes vehicle detection into segmentation-based vehicle localization and generalized zero-shot vehicle classification. Particularly, the proposed framework can well handle the problem of generalized zero-shot vehicle detection, which is challenging due to the requirement of recognizing vehicles that are even unseen during training. Specifically, a hierarchical DeepLab v3 model is proposed in the framework, which fully exploits fine-grained features to locate the target on a pixel-wise level, then recognizes vehicles in a coarse-grained manner. Additionally, the hierarchical DeepLab v3 model is beneficially compatible to combine the generalized zero-shot recognition. To the best of our knowledge, there is no publically available dataset to test comparative methods, we therefore construct a new dataset to fill this gap of evaluation. The experimental results show that the proposed framework yields promising results on the imperative yet difficult task of zero-shot vehicle detection and recognition.

scholarly journals ORIENTED VEHICLE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON FEATURE AMPLIFICATION AND CATEGORY BALANCE BY OVERSAMPLING DATA AUGMENTATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 153-159
Author(s):  
N. Mo ◽  
L. Yan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Vehicle Detection ◽  
Training Dataset ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Fine Grained ◽  
Bounding Boxes ◽  
The Impact ◽  
Oriented Bounding Boxes

Abstract. Vehicles usually lack detailed information and are difficult to be trained on the high-resolution remote sensing images because of small size. In addition, vehicles contain multiple fine-grained categories that are slightly different, randomly located and oriented. Therefore, it is difficult to locate and identify these fine categories of vehicles. Considering the above problems in high-resolution remote sensing images, this paper proposes an oriented vehicle detection approach. First of all, we propose an oversampling and stitching method to augment the training dataset by increasing the frequency of objects with fewer training samples in order to balance the number of objects in each fine-grained vehicle category. Then considering the effect of the pooling operations on representing small objects, we propose to improve the resolution of feature maps so that detailed information hidden in feature maps can be enriched and they can better distinguish the fine-grained vehicle categories. Finally, we design a joint training loss function for horizontal and oriented bounding boxes with center loss, to decrease the impact of small between-class diversity on vehicle detection. Experimental verification is performed on the VEDAI dataset consisting of 9 fine-grained vehicle categories so as to evaluate the proposed framework. The experimental results show that the proposed framework performs better than most of competitive approaches in terms of a mean average precision of 60.7% and 60.4% in detecting horizontal and oriented bounding boxes respectively.

scholarly journals An Image is Worth a Thousand Species: Scaling high-resolution plant biodiversity prediction to biome-level using citizen science data and remote sensing imagery

2021 ◽  
Vol 5 ◽  
Author(s):  
Lauren Gillespie ◽  
Megan Ruffley ◽  
Moisés Expósito-Alonso
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Citizen Science ◽  
Environmental Variables ◽  
Aerial Imagery ◽  
Coarse Grained ◽  
Plant Biodiversity ◽  
Distribution Models ◽  
Remote Sensing Imagery ◽  
Bioclimatic Variables

Accurately mapping biodiversity at high resolution across ecosystems has been a historically difficult task. One major hurdle to accurate biodiversity modeling is that there is a power law relationship between the abundance of different types of species in an environment, with few species being relatively abundant while many species are more rare. This “commonness of rarity,” confounded with differential detectability of species, can lead to misestimations of where a species lives. To overcome these confounding factors, many biodiversity models employ species distribution models (SDMs) to predict the full extent of where a species lives, using observations of where a species has been found, correlated with environmental variables. Most SDMs use bioclimatic environmental variables as the dependent variable to predict a species’ range, but these approaches often rely on biased pseudo-absence generation methods and model species using coarse-grained bioclimatic variables with a useful resolution floor of 1 km-pixel. Here, we pair iNaturalist citizen science plant observations from the Global Biodiversity Information Facility with RGB-Infrared aerial imagery from the National Aerial Imagery Program to develop a deep convolutional neural network model that can predict the presence of nearly 2,500 plant species across California. We utilize a state-of-the-art multilabel image recognition model from the computer vision community, paired with a cutting-edge multilabel classification loss, which leads to comparable or better accuracy to traditional SDM models, but at a resolution of 250m (Ben-Baruch et al. 2020, Ridnik et al. 2020). Furthermore, this deep convolutional model is able to accurately predict species presence across multiple biomes of California with good accuracy and can be used to build a plant biodiversity map across California with unparalleled accuracy. Given the widespread availability of citizen science observations and remote sensing imagery across the globe, this deep learning-enabled method could be deployed to automatically map biodiversity at large scales.

scholarly journals Weakly Supervised Change Detection Based on Edge Mapping and SDAE Network in High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (23) ◽  
pp. 3907
Author(s):  
Ning Lu ◽  
Can Chen ◽  
Wenbo Shi ◽  
Junwei Zhang ◽  
Jianfeng Ma
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
High Efficiency ◽  
Deep Structure ◽  
Ground Truth ◽  
Classification Model ◽  
Remote Sensing Images ◽  
Pixel Pair ◽  
Weakly Supervised

Change detection for high-resolution remote sensing images is more and more widespread in the application of monitoring the Earth’s surface. However, on the one hand, the ground truth could facilitate the distinction between changed and unchanged areas, but it is hard to acquire them. On the other hand, due to the complexity of remote sensing images, it is difficult to extract features of difference, let alone the construction of the classification model that performs change detection based on the features of difference in each pixel pair. Aiming at these challenges, this paper proposes a weakly supervised change detection method based on edge mapping and Stacked Denoising Auto-Encoders (SDAE) network called EM-SDAE. We analyze the difference in edge maps of bi-temporal remote sensing images to acquire part of the ground truth at a relatively low cost. Moreover, we design a neural network based on SDAE with a deep structure, which extracts the features of difference so as to efficiently classify changed and unchanged regions after being trained with the ground truth. In our experiments, three real sets of high-resolution remote sensing images are employed to validate the high efficiency of our proposed method. The results show that accuracy can even reach up to 91.18% with our method. In particular, compared with the state-of-the-art work (e.g., IR-MAD, PCA-k-means, CaffeNet, USFA, and DSFA), it improves the Kappa coefficient by 27.19% on average.

Characteristics and origins of ridges and troughs on the top of the Middle Miocene strata in the Beijiao sag of the Qiongdongnan basin, northern South China Sea

2020 ◽  
pp. 1-57
Author(s):  
Yufeng Li ◽  
Renhai Pu ◽  
Gongcheng Zhang ◽  
Hongjun Qu
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Middle Miocene ◽  
Northern South China Sea ◽  
Qiongdongnan Basin ◽  
Coarse Grained ◽  
Bottom Currents ◽  
Fine Grained ◽  
Sediment Waves ◽  
History Of

Sedimentary structures generated by bottom currents are poorly understood worldwide. Ridges and troughs are imaged for the first time by 3D high-resolution seismic data and drilled by a well, YL19-1-1, in the Beijiao sag of Qiongdongnan basin (QDNB). Combined with 2D high resolution seismic data, they are analyzed in detail. The results show that ridges and troughs occur on the top of the Middle Miocene, dominantly present a wave-shaped structure. Their magnitudes are larger on the middle (regional) slope than on the upper and lower slope. They extend for tens of kilometers, dominantly parallel to one another, evenly spaced and nearly E-W directed distribution, some of which locally merge and bifurcate. They are aligned oblique to the regional slope. Both internal mounded reflections and parallel underlying-strata reflections, occur within ridges. The presence of polygonal faults and weak-to-moderate amplitudes within the ridges and troughs, suggests that they consist of fine-grained mudstones, as confirmed by well YL19-1-1. High amplitudes filled within troughs are probably composed of coarse-grained turbidite sandstones where polygonal faults are inhibited. Truncated reflections and onlaps occur along the thalweg of a trough, and are also clearly observed on the sides of ridges and troughs. We conclude the troughs are a product of erosion of bottom currents, and ridges are remnant underlying (sediment waves) strata as a result of this erosion. Besides, troughs are filled by turbidite sandstones with high amplitudes in the southwestern part of the study area, where ridges and troughs a combined result of early erosion by bottom currents and later reworking by turbidity flows. Conceptual schematic models are proposed to show the evolutionary history of ridges and troughs. This study provides new insights into further understanding of erosion and deposition of bottom currents.

Parallel Solution of Magnetotelluric Occam Inversion Algorithm Based on Hybrid MPI/OpenMP Model

2014 ◽  
Vol 602-605 ◽  
pp. 3751-3754
Author(s):  
Yu Liu ◽  
Yi Xiao
Keyword(s):  
Parallel Algorithm ◽  
High Efficiency ◽  
Programming Model ◽  
Operation Time ◽  
Coarse Grained ◽  
Inversion Algorithm ◽  
Parallel Solution ◽  
Fine Grained ◽  
Parallel Programming Model ◽  
Occam Inversion

In order to improve the efficiency of magnetotelluric Occam inversion algorithm (MT Occam), a parallel algorithm is implemented on a hybrid MPI/OpenMP parallel programming model to increase its convergence speed and to decrease the operation time. MT Occam is partitioned to map the task on the parallel model. The parallel algorithm implements the coarse-grained parallelism between computation nodes and fine-grained parallelism between cores within each node. By analyzing the data dependency, the computing tasks are accurately partitioned so as to reduce transmission time. The experimental results show that with the increase of model scale, higher speedup can be obtained. The high efficiency of the parallel partitioning strategy of the model can improve the scalability of the parallel algorithm.

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