In Press Peer Reviewed Articles

As a convenience to ASPRS members, in-press peer reviewed articles approved for publication in forthcoming issues of PE&RS are listed below.

Manuscript number: 23-00045R2
Research Progress of Optical Satellite Remote Sensing Monitoring Asphalt Pavement Aging
Jingwen Wang, Dayong Yang, Zhiwei Xie, Han Wang, Zhigang Hao, Fanyu Zhou, and Xiaona Wang

The aging condition of asphalt pavement is an invaluable basis for traffic infrastructure evaluation. Due to the amount of time and high cost of monitoring and identifying asphalt pavement aging, many current studies focus on satellite remote sensing methods. In this paper, some methods and technologies for monitoring asphalt pavement degradation by optical satellite remote sensing are introduced as a literature review. Many researchers have developed spectrum libraries based on the actual aging of asphalt pavements, and it is possible to construct pavement health indices based on spectrum changes. Some indexes can extract different aging degrees of asphalt pavement from optical satellite images. Of course, current research can only preliminarily reflect the aging phenomenon of asphalt pavement and cannot accurately describe the distress characteristics of asphalt pavement. Future research needs to further strengthen mechanism research, develop higher resolution images, improve image processing technology, and adopt multi-means fusion analysis methods.

Manuscript number: 23-00038R2
Mapping Winter Wheat Using Ensemble-Based Positive Unlabeled Learning Ap-proach
Hanxiang Wang, Fan Yu, Junwei Xie, Huawei Wan, and Haotian Zheng

High-resolution remote sensing images can support machine learning methods to achieve remarkable results in agricultural monitoring. However, traditional supervised learning methods require pre-labeled training data and are unsuitable for non-fully labeled areas. Positive and Unlabeled Learning (PUL), can deal with unlabeled data. A loss function PU-Loss was proposed in this study to directly optimize the PUL evaluation metric and to address the data imbalance problem caused by unlabeled positive samples. Moreover, a hybrid normalization module Batch-Instance-Layer Normalization was pro-posed to perform multiple normalization methods based on the resolution size and to improve the model performance further. A real-world positive and unlabeled winter wheat data set was used to evaluate the proposed method, which outperformed widely used models such as U-Net, DeepLabv3+, and DA-Net. The results demonstrated the potential of PUL for winter wheat identification in remote sensing images.

Manuscript number: 23-00079R2
Building Shadow Detection Based on Improved Quick Shift Algorithm in GF-2 Images
Yunzhi Chen, Chao Wang, Wei Wang, Xiang Zhang, and Nengcheng Chen

Shadows in remote sensing images contain crucial information about various features on the ground. In this study, a method for detecting building shadows in GF-2 images based on improved quick shift was proposed. First, six feature variables were constructed: first principal component (PC1), brightness component (I), normalized difference shadow index (NDSI), morphological shadow index (MSI), normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Then, the image was segmented to obtain homogeneous objects, which were then classified using a random forest model. Two improvements were added to the quick shift algorithm: using PC1, I, and MSI as input data instead of RGB images; and adding Canny edge constraints. Validation in six research areas yields Kappa coefficients of 0.928, 0.896, 0.89, 0.913, 0.879, and 0.909, confirming method feasibility. In addition, comparative experiments demonstrate its effectiveness and robustness across different land cover types while mitigating the segmentation scale effect.

Manuscript number: 24-00005R2
Hyperspectral Reflectance Assessment for Preliminary Identification of Degraded Soil Zones in Industrial Sites, India
Amitava Dutta, Rashi Tyagi, Shilpi Sharma, and Manoj Datta

Manuscript number: 24-00006R2
One-Dimensional-Mixed Convolution Neural Network and Covariance Pooling Model for Mineral Mapping of Porphyry Copper Deposit Using PRISMA Hyperspectral Data
Sima Peyghambari, Yun Zhang, Hassan Heidarian, and Milad Sekandari

Mapping distribution of alterations around porphyry copper deposits (PCDs) greatly affects mineral exploration. Diverse geological processes generate irregular alteration patterns with diverse spectral characteristics in mineral deposits. Applying remotely sensed hyperspectral images (HSIs) is an appealing technology for geologic surveyors to generate alteration maps. Conventional methods mainly use shallow spectral absorption features to discriminate minerals and cannot extract their important spectral information. Deep neural networks with nonlinear layers can evoke the deep spectral and spatial information of HSIs. Deep learning–based methods include fully connected neural networks, convolutional neural networks, and hybrid convolutional networks like mixed convolution neural network and covariance pooling (MCNN-CP) algorithms. However, each has its advantages and limitations. To significantly avoid losing important spectral features, we proposed a new method by fusing a one-dimensional convolutional neural network (1D-CNN) with MCNN-CP (1D-MCNN-CP), achieving an overall accuracy (97.44%) of mineral mapping from PRISMA HSIs. This research deduced that 1D-MCNN-CP improved performance and reduced misclassification errors among minerals sharing similar spectral features.

Manuscript number: 24-00008R2
Development of an Automatic Feature Point Classification Method for Three-Dimensional Mapping Around Slewing and Derricking Cranes
Hisakazu Shigemori, Junichi Susaki, Mizuki Yoneda, and Marek Ososinski

Crane automation requires a three-dimensional (3D) map around cranes that should be reconstructed and updated quickly. In this study, a high-precision classification method was developed to distinguish stationary objects from moving objects in moving images captured by a monocular camera to stabilize 3D reconstruction. To develop the method, a moving image was captured while the crane was slewed with a monocular camera mounted vertically downward at the tip of the crane. The boom length and angle data were output from a control device, a controller area network. For efficient development, a simulator that imitated the environment of an actual machine was developed and used. The proposed method uses optical flow to track feature points. The classification was performed successfully, independent of derricking motion. Consequently, the proposed method contributes to stable 3D mapping around cranes in construction sites.

Manuscript number: 23-00076R2
Semantic Segmentation of Point Cloud Scene via Multi-Scale Feature Aggregation and Adaptive Fusion
Baoyun Guo, Xiaokai Sun, Cailin Li, Na Sun, Yue Wang, and Yukai Yao

Point cloud semantic segmentation is a key step in 3D scene understanding and analysis. In recent years, deep learning–based point cloud semantic segmentation methods have received extensive attention from researchers. Multi-scale neighborhood feature learning methods are suitable for inhomogeneous density point clouds, but different scale branching feature learning increases the computational complexity and makes it difficult to accurately fuse different scale features to express local information. In this study, a point cloud semantic segmentation network based on RandLA-Net with multi-scale local feature aggregation and adaptive fusion is proposed. The designed structure can reduce computational complexity and accurately express local features. The mean intersection-over-union is improved by 1.1% on the SemanticKITTI data set with an inference speed of nine frames per second, while the mean intersection-over-union is improved by 0.9% on the S3DIS data set, compared with RandLA-Net. We also conduct ablation studies to validate the effectiveness of the proposed key structure.

Manuscript number: 23-00086R2
A Robust Star Identification Algorithm for Resident Space Object Surveillance
Liang Wu, Pengyu Hao, Kaixuan Zhang, Qian Zhang, Ru Han, and Dekun Cao

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an eﬀicient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance.

Manuscript number: 24-00002R2
Wavelets for Self-Calibration of Aerial Metric Camera Systems
Jun-Fu Ye, Jaan-Rong Tsay, and Dieter Fritsch

In this paper, wavelets are applied to develop new models for the self-calibration of aerial metric camera systems. It is well known and mathematically proven that additional parameters (APs) can compensate image distortions and remaining error sources by a rigorous photogrammetric bundle-block adjustment. Thus, kernel functions based on orthogonal wavelets (e.g., asymmetric Daubechies wavelets, least asymmetric Daubechies wavelets, Battle-Lemarié wavelets, Meyer wavelets) are used to build the wavelets-based family of APs for self-calibrating digital frame cameras. These new APs are called wavelet APs. Its applications in rigorous tests are accomplished by using aerial images taken by an airborne digital mapping camera in situ and practical calibrations. The test results demonstrate that these orthogonal wavelet APs are applicable and largely avoid the risk of over-parameterization. Their external accuracy is evaluated using reliable and high precision check points in the calibration field.

Manuscript number: 24-00015R2
Attention Heat Map-Based Black-Box Local Adversarial Attack for Synthetic Aperture Radar Target Recognition
Xuanshen Wan, Wei Liu, Chaoyang Niu, and Wanjie Lu

Synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNNs) are susceptible to adversarial attacks. In this study, we proposed an SAR black-box local adversarial attack algorithm named attention heat map-based black-box local adversarial attack (AH-BLAA). First, we designed an attention heat map extraction module combined with the layer-wise relevance propagation (LRP) algorithm to obtain the high concerning areas of the SAR-ATR models. Then, to generate SAR adversarial attack examples, we designed a perturbation generator module, introducing the structural dissimilarity (DSSIM) metric in the loss function to limit image distortion and the differential evolution (DE) algorithm to search for optimal perturbations. Experimental results on the MSTAR and FUSAR-Ship datasets showed that compared with existing adversarial attack algorithms, the attack success rate of the AH-BLAA algorithm increased by 0.63% to 33.59% and 1.05% to 17.65%, respectively. Moreover, the lowest perturbation ratios reached 0.23% and 0.13%, respectively.