Multi-scale Segmentation Method Research Articles

Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction (LSP). To illustrate various study area scales, Ganzhou City in China, its eastern region (Ganzhou East), and Ruijin County in Ganzhou East were chosen. Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m, as well as slope units that were extracted by multi-scale segmentation method. The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs. Then, landslide susceptibility maps (LSMs) of Ganzhou City, Ganzhou East and Ruijin County are produced using a support vector machine (SVM) and random forest (RF), respectively. The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City, along with the LSMs of Ruijin County from Ganzhou East. Additionally, LSMs of Ruijin at various mapping unit scales are generated in accordance. Accuracy and landslide susceptibility indexes (LSIs) distribution are used to express LSP uncertainties. The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City, Ganzhou East to Ruijin County, whereas those under slope units are less affected by study area scales. Of course, attentions should also be paid to the broader representativeness of large study areas. The LSP accuracy of slope units increases by about 6%–10% compared with those under grid units with 30 m and 60 m resolution in the same study area's scale. The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large. The importance of environmental factors varies greatly with the 60 m grid unit, but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.Graphic abstract

Semi-supervised segmentation of multi-scale soil pores based on a novel receptive field structure

The study of soil pores using computed tomography (CT) technology and deep learning has been proven to be effective. However, conventional neural networks struggle to accurately segment both large connected pores and small scatter pores in soil CT images at different scales. To address this limitation, this paper proposes a semi-supervised multi-scale segmentation method (SMS). The SMS method comprises two parts: 1) Upstream multi-scale pore segmentation branches that generate multi-feature pore segmentation maps based on a novel receptive field structure, and 2) Downstream semi-supervised classification task that selects the optimal pore feature map for output. Experimental results demonstrate that SMS effectively enhances segmentation performance for multi-scale soil pores, achieving the highest accuracy, precision, recall, and F1-score values of 99.55 %, 83.76 %, 96.12 %, and 89.35 %, respectively. These results outperform four common deep learning methods and three traditional image processing software. This study represents a significant advancement in high-precision automated segmentation of soil pores, providing valuable image processing support for investigating soil structure characteristics, soil conservation, and soil ecosystem services.

Mountain Segmentation Based on Global Optimization with the Cloth Simulation Constraint

Mountains are an important research object for surveying, mapping, cartography, space science, and ecological remote sensing. Automatic mountain segmentation is one of the most critical techniques in large-scale mountain analyses. However, several factors limit the segmentation accuracy, such as the complexity of mountains, the noise of geospatial data, and the confusion in distinguishing non-mountainous objects with similar features. In order to improve the accuracy of mountain segmentation against these limiting factors, we impose the cloth constraint over the digital elevation model (DEM) with the underlying assumption that the mountain has a sizeable relative elevation and slope. We propose a robust mountain segmentation method with the cloth simulation constraint. The core algorithm extracts the relative elevation of the region using a cloth simulation filtering algorithm by transforming the mountain segmentation problem into an optimization problem based on the global energy function consisting of the relative elevation and slope. Experiments on a wide range of Earth and lunar elevation datasets with some of the aforementioned limitations show that the proposed method can extract complex mountain baselines, avoid the misclassification of lunar craters, and significantly improve the robustness and accuracy of mountain segmentation. Compared to three state-of-the-art methods (the Lunar Mountain Detection Method, the Landform Mask Method in SNAP™ from European Space Agency (located in Paris, France), and the Multiscale Segmentation Method in eCognition™ from Definiens Imaging (located in Munich, Germany), the F1 and IoU improved by 14.70% and 20.46% on average and 29.07% and 38.94% at most, respectively, which validates that the proposed method has a better all-around performance.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient and automatic multi-scale segmentation (MSS) method proposed by the authors promotes the application of slope units. However, LSP modeling based on these slope units has not been performed. Moreover, the heterogeneity of conditioning factors in slope units is neglected, leading to incomplete input variables of LSP modeling. In this study, the slope units extracted by the MSS method are used to construct LSP modeling, and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean, standard deviation and range. Thus, slope units-based machine learning models considering internal variations of conditioning factors (variant slope-machine learning) are proposed. The Chongyi County is selected as the case study and is divided into 53,055 slope units. Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations. Random forest (RF) and multi-layer perceptron (MLP) machine learning models are used to construct variant Slope-RF and Slope-MLP models. Meanwhile, the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors, and conventional grid units-based machine learning (Grid-RF and MLP) models are built for comparisons through the LSP performance assessments. Results show that the variant Slope-machine learning models have higher LSP performances than Slope-machine learning models; LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models. It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling, and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides. The research results have important reference significance for land use and landslide prevention. ➢ Slope units extracted by multi-scale segmentation method are appropriate for LSP; ➢ Heterogeneity of conditioning factors are reflected by mean, range and standard deviation values; ➢ Variant Slope-machine learning models have higher LSP accuracy than Slope-machine learning models; ➢ Slope-machine learning models have stronger engineering practicability than Grid-machine learning models.

Automatic Coastline Extraction Based on the Improved Instantaneous Waterline Extraction Method and Correction Criteria Using SAR Imagery

Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline extraction method was proposed in this paper with an integrated Otsu threshold method, a region-growing algorithm, Canny edge detection, and a morphology operator. Based on SAR feature extraction and screening, the multi-scale segmentation method and KNN classification algorithms were used to achieve object-oriented automatic classification. According to different types of ground features, the correction criteria were presented and used in correcting the instantaneous waterline in biological coasts and undeveloped silty coasts. As a result, the accurate extraction of the coastline was accomplished in the area of the Yellow River Delta. The coastline was compared with that extracted from the GF-1 optical image. The result shows that the deviation degree was less than the field distance represented by three pixels.

Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images.

Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction.

Multi-Scale Segmentation Method of Remote Sensing Big Data Image Using Deep Learning

Remote sensing image (RSI) segmentation is an effective method to interpret remote sensing information and an important means of remote sensing data information processing. Traditional RSI segmentation methods have some problems such as poor segmentation accuracy and low similarity difference measurement. Therefore, we propose a multi-scale segmentation (MSS) method for remote sensing big data image. First, the segmentation scale of RSI is divided, and the quantitative value of histogram band is used to calculate the similarity index between different objects; Second, the parameters in the same spot are improved based on the maximum area method to determine the shape factor of RSI; Finally, the object closure model is established to clarify the region conversion cost, and the RSI is dynamically segmented based on Multi-scale convolutional neural networks; The MSS algorithm of RSI is designed, and the MSS method of RSI is obtained. The results show that the maximum similarity difference measure of the proposed method is 0.648, and the similarity difference measure always remains the largest. The maximum recall of RSI is 0.954, and the highest recall is 0.988, indicating that the RSI segmentation accuracy of the proposed method is good.

Development of a core feature identification application based on the Faster R-CNN algorithm

As engineering rock mass quality assessment is an important part of the evaluation of deposit mining technical conditions, the identification and counting of core features are essential but time-consuming, and the complexity of core features leads to the invalidity of traditional image processing programs. In such a case, we developed an efficient automated core feature identification and counting application by adopting the Faster R-CNN algorithm together with a self-designed batch processing and counting program, which allows for the high-speed identification of target features among many pictures and can count and output formatted identification results. The evaluation results show that this application can significantly improve the identification accuracy and speed up the process with the help of a deep learning algorithm and our computer program.In the comparison and selection of the Faster R-CNN and YOLO algorithms, YOLO was eliminated due to poor performance. The main reason is that the multiscale self-similarity of core features has adverse effects on the multiscale segmentation method of the YOLO algorithm, making YOLO identify one feature repeatedly.The overall training evaluation F1-score of the Faster R-CNN-based AI model reaches 0.91, showing an ideal result. In the practical test, the overall AI identification F1-score is 0.93, and the application processing F1-score reaches 0.92. The time complexities of the AI model and application are both acceptable, with T(n) = O(n). In terms of identification speed, the application process is 48 times faster than that of manual identification.

A Multi-Scale Brain Tumor Segmentation Method based on U-Net Network

Brain tumor is a serious threat to human health. Because the size and shape of brain tumors can be uneven, irregular and unstructured. Automatic segmentation of tumors from magnetic resonance imaging (MRI) is a challenging task. Brain tumor segmentation using computer-aided diagnosis has important clinical significance for the prognosis and treatment of patients. The traditional U-Net network can not take full advantage of context information, which is easy to cause the loss of effective information of image. Therefore, we propose a multi-scale segmentation method for brain tumors based on U-Net network, in which a multi-scale module for feature extraction is added between down-sampling and up-sampling. The brain tumor public dataset BraTS2020 is used for testing. Dice coefficient was used as the evaluation index, and our experimental method made the average dice of the whole tumor area, core tumor area and enhanced tumor area reach 87.57%, 86.20% and 84.24%, respectively. The experimental results show that the proposed method has comparable precision with typical brain tumor segmentation methods.

Extracting Wetland Type Information with a Deep Convolutional Neural Network.

Wetlands have important ecological value. The application of wetland remote sensing is essential for the timely and accurate analysis of the current situation in wetlands and dynamic changes in wetland resources, but high-resolution remote sensing images display nonobvious boundaries between wetland types. However, high classification accuracy and time efficiency cannot be guaranteed simultaneously. Extraction of wetland type information based on high-spatial-resolution remote sensing images is a bottleneck that has hindered wetland development research and change detection. This paper proposes an automatic and efficient method for extracting wetland type information. First, the object-oriented multiscale segmentation method is used to realize the fine segmentation of high-resolution remote sensing images, and then the deep convolutional neural network model AlexNet is used to classify automatically the types of wetland images. The method is verified in a case study involving field-measured data, and the classification results are compared with those of traditional classification methods. The results show that the proposed method can more accurately and efficiently extract different wetland types in high-resolution remote sensing images than the traditional classification methods. The proposed method will be helpful in the extension and application of wetland remote sensing technology and will provide technical support for the protection, development, and utilization of wetland resources.

Multi-scale organs image segmentation method improved by squeeze-and-attention based on partially supervised learning

Radiotherapy is one of most treatments for tumors. To accurately control the radiation dose distribution and lessen the radiation damage to normal tissues and organs in radiotherapy, it is essential to delineate organs at risk (OARs) precisely. However, manual delineating and some traditional methods are labor-intensive and time-consuming. There is an urgent need for fast and precise segmentation methods in radiotherapy. This paper proposes a fully automatic segmentation method based on the 3D U-Net for multi-organ in head and neck. It introduces squeeze-and-attention blocks to gather multi-scale context information and the receptive field block to balance the performance between large-sized and small-sized organs. Furthermore, it is trained by the marginal and exclusion loss function in a partially supervised learning mode. We evaluated the model with dice similarity coefficient (DSC), 95% Hausdorff distance (95HD) and inference time. Its average DSC is 0.829, which is 4.5%, 3.2%, and 2.4% higher than AnatomyNet's, nnU-net's, and FocusNet's, respectively, and its average 95HD is 2.19. Moreover, its inference time and parameters are 63% and 60% less than FocusNetv2's. For the segmentation of OARs in head and neck, our model is more accurate than AnatomyNet, faster than FocusNetv2, and better balances between segmentation accuracy and inference time. It demonstrates that our method is more applicable for clinical treatment.

A Modified Residual Network Based on Multi-scale Segmentation for Aerobics Motion Image Recognition

<p>Image recognition is an important field in artificial intelligence, it makes use of the computer to conduct image processing, analysis and understanding to recognize a variety of different objects. And it uses a series of enhancement and reconstruction methods to effectively improve the image quality. Traditional deep Convolutional Neural Network (DCNN) not only improves the recognition accuracy, but also reduces the recognition speed. How to improve the speed while maintaining the accuracy has become an important direction in image recognition. In this paper, we propose a modified Residual network based on multi-scale segmentation for aerobics motion image recognition. The new residual network has the characteristics of shorter network length and faster recognition speed. First, it reduces the length of the network and gets a new residual network with seven layers. Then, combining with the multi-scale segmentation method, an image recognition residual network is obtained. Finally, experiments on the CIFAR10 dataset, the results show that the proposed new motion image recognition method has better recognition accuracy and faster recognition speed.</p> <p> </p>

Research on classification method based on multi-scale segmentation and hierarchical classification

This paper transmission line corridors covering area in Hebei north area as the research object to explore multi-scale segmentation threshold suitable for Hebei north image, found applicable to Hebei north region segmentation threshold rules. Main methods are the object-oriented multi-scale segmentation and hierarchical classification, using image segmentation principle, make full use of high resolution image rich features such as shape, texture, object relationships. It is used for the follow-up investigation of hidden danger of external damage of power transmission channel in northern Hebei region. The main conclusions of the experiment are as follows: 1. By comparing and analyzing the results of five groups of different thresholds (40, 50, 60, 70 and 80), it is concluded that the single threshold of the multi-scale segmentation method suitable for most mountainous images in the study area is 60, and this method can achieve high-precision ground object classification for images in northern Hebei region. 2. The ground feature cover classification method that is more suitable for the parallel processing of a large number of study areas is the object-oriented hierarchical classification method, and preliminary exploration results of detailed parameters have been achieved.

Pine wilt disease detection in high-resolution UAV images using object-oriented classification

Pine wilt disease (PWD) is currently one of the main causes of large-scale forest destruction. To control the spread of PWD, it is essential to detect affected pine trees quickly. This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD. We used an unmanned aerial vehicle (UAV) platform equipped with an RGB digital camera to obtain high spatial resolution images, and multi-scale segmentation was applied to delineate the tree crown, coupling the use of object-oriented classification to classify trees discolored by PWD. Then, the optimal segmentation scale was implemented using the estimation of scale parameter (ESP2) plug-in. The feature space of the segmentation results was optimized, and appropriate features were selected for classification. The results showed that the optimal scale, shape, and compactness values of the tree crown segmentation algorithm were 56, 0.5, and 0.8, respectively. The producer’s accuracy (PA), user’s accuracy (UA), and F1 score were 0.722, 0.605, and 0.658, respectively. There were no significant classification errors in the final classification results, and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation. The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing. This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments

Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments

Object-based spectral-phenological features for mapping invasive Spartina alterniflora

Spartina alterniflora (S. alterniflora), a prevailing invasive species in the coastal zones, has resulted in significant ecosystem degradation and economic losses since its introduction to China in 1979. Among the existing studies that incorporate remote sensing to map S. alterniflora, the pixel-based phenological feature composite method (Ppf-CM) has proved to be successful due to its merits to overcome cloud contamination while exaggerating the spectral separability between S. alterniflora and its co-dominant native species. However, one major limitation of the Ppf-CM method is that it extracts phenological features from a single-pixel without accounting for its surrounding geospatial information that proved essential for more accurate mapping. In this study, we aim to ameliorate this problem by incorporating geometric, texture, and contextual information. To this end, we developed a new object-based phenological feature composite method (OPpf-CM). Specifically, we first generated a composite image by stacking two images that were respectively acquired during the distinctive phenological periods of S. alterniflora. Then we derived spectrally homogenous objects on the composite image through an unsupervised multiscale segmentation method. Various features derived from objects served as the input of support vector machine (SVM) classifier to produce a S. alterniflora map. To evaluate the performance of OPpf-CM, we carried out a comparison of classification performance between our developed OPpf-CM method and the previously developed Ppf-CM one (Tian et al., 2020) with the aid of visual interpretation and accuracy statistics (i.e. confusion matrix). The results showed that OPpf-CM achieved a higher overall accuracy (98%) than Ppf-CM (96.67%) when validated with the ground reference data that was visually interpreted by the high spatial resolution imagery and field survey. Meanwhile, the identification of fragmented S. alterniflora patches along with mixed-cover areas can be significantly improved with the OPpf-CM method. We envision that the developed object-based spectral-phenological feature has the potential to be applied to mapping a wide spectrum of coastal vegetations.

Dynamic monitoring algorithm of natural resources in scenic spots based on MODIS Remote Sensing technology

A dynamic monitoring algorithm of natural resources in scenic spots based on MODIS remote sensing technology is proposed to improve natural resources monitoring accuracy in scenic spots. The remote sensing images of scenic spots obtained by MODIS were preprocessed by TM image processing, atmospheric correction, and other technologies to get high-precision remote sensing images. The remote sensing images of scenic spots were segmented by the multi-scale segmentation method, and then the hierarchical supervision classification method was used. The change points of natural resources were extracted. The resource changes and independent variables of scenic spots were analyzed based on the least square method to realize the dynamic monitoring of natural resources in scenic locations. The experimental results show that the technique can accurately monitor the dynamic changes of forest resources and water resources in scenic spots, and the monitoring results have high accuracy.

Automatic Extraction of Green Tide Using Dual Polarization Chinese GF-3 SAR Images

Yu, H.F.; Wang, C.Y.; Sui, Y.; Li, J.H., and Chu, J.L., 2020. Automatic extraction of green tide using dual polarization China GF-3 SAR images. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T.W. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 318-325. Coconut Creek (Florida), ISSN 0749-0208.Traditional optical images are not suitable for the all-weather observation of green tide due to the fact that they are greatly affected by clouds. Synthetic aperture radar (SAR) on the other hand, is able to realize all-day and all-weather observation due to a lower sensitivity to clouds, rain and fog. SAR images are therefore an effective supplementary data for the monitoring of green tide. Due to the differences in brightness and noise across the different areas in an image, it is difficult to use the same threshold in order to extract all the green tide information from the image. Based on the iterative threshold method and the histogram bimodal method, this paper presents a new method for the automatic detection of green tide that uses adaptive thresholds for Gaofen-3 (GF-3) satellite dual polarization SAR remote sensing images. In this study, firstly, a sliding window is used to segment the image into multiple sub-images of the same size; secondly, the iterative threshold method is used to obtain the green tide and seawater samples that have the theoretical bimodal structure from each sub-image; then, using the histogram bimodal method, the detection threshold is calculated automatically; and finally, using the threshold segmentation, the green tide areas are extracted from the images. In order to verify the proposed method, the multi-scale segmentation method and the proposed method are both used to detect green tide in China Yellow Sea. The study results show that in the case of GF-3 SAR FSII images, the proposed automatic detection method is superior to the multi-scale segmentation method, as it not only improves the accuracy of green tide detection, but also realizes the automation of green tide extraction. Furthermore, Cross-polarization (HV) images may be more suitable than co-polarization (HH) images for the extraction of green tide due to their lower noise level.

Object-Oriented Open-Pit Mine Mapping Using Gaofen-2 Satellite Image and Convolutional Neural Network, for the Yuzhou City, China

Our society’s growing need for mineral resources brings with it the associated risk of degrading our natural environment as well as impacting on neighboring communities. To better manage this risk, especially for open-pit mine (OM) operations, new earth observation tools are required for more accurate baseline mapping and subsequent monitoring. The purpose of this paper is to propose an object-oriented open-pit mine mapping (OOMM) framework from Gaofen-2 (GF-2) high-spatial resolution satellite image (HSRSI), based on convolutional neural networks (CNNs). To better present the different land use categories (LUCs) in the OM area, a minimum heterogeneity criterion-based multi-scale segmentation method was used, while a mean area ratio method was applied to optimize the segmentation scale of each LUC. After image segmentation, three object-feature domains were obtained based on the GF-2 HSRSI: spectral, texture, and geometric features. Then, the gradient boosting decision tree and Pearson correlation coefficient were used as an object feature information reduction (FIR) method to recognize the distinguishing feature that describe open-pit mines (OMs). Finally, the CNN was used by combing the significant features to map the OM. In total, 105 OM sites were extracted from the interpretation of GF-2 HSRSIs and the boundary of each OM was validated by field work and used as inputs to evaluate the open-pit mine mapping (OMM) accuracy. The results revealed that: (1) the FIR tool made a positive impact on effective OMM; (2) by splitting the segmented objects into two groups, training and testing sets which are composed of 70% of the objects, and validation sets which are formed by the remaining 30% of the objects, then combing the selected feature subsets for training to achieve an overall accuracy (OA) of 90.13% and a Kappa coefficient (KC) of 0.88 of the whole datasets; (3) comparing the results of the state-of-the-art method, support vector machine (SVM), in OMM, the proposed framework outperformed SVM by more than 7.28% in OA, 8.64% in KC, 6.15% in producer accuracy of OM and by 9.31% in user accuracy of OM. To the best of our knowledge, it is the first time that OM information has been used through the integration of multiscale segmentation of HSRSI with the CNN to get OMM results. The proposed framework can not only provide reliable technical support for the scientific management and environmental monitoring of open pit mining areas, but also be of wide generality and be applicable to other kinds of land use mapping in mining areas using HSR images.

A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images.

In this study, an essential application of remote sensing using deep learning functionality is presented. Gaofen-1 satellite mission, developed by the China National Space Administration (CNSA) for the civilian high-definition Earth observation satellite program, provides near-real-time observations for geographical mapping, environment surveying, and climate change monitoring. Cloud and cloud shadow segmentation are a crucial element to enable automatic near-real-time processing of Gaofen-1 images, and therefore, their performances must be accurately validated. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. The proposed method first implements feature map based on the spectral-spatial features from residual convolutional layers and the cloud/cloud shadow footprints extraction based on a novel loss function to generate the final footprints. The experimental results using Gaofen-1 images demonstrate the more reasonable accuracy and efficient computational cost achievement of the proposed method compared to the cloud and cloud shadow segmentation performance of two existing state-of-the-art methods.