Atmospheric Light Estimation Based Remote Sensing Image Dehazing

Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.

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Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network

Remote Sensing ◽

10.3390/rs11243008 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3008 ◽

Cited By ~ 2

Author(s):

Ziqi Gu ◽

Zongqian Zhan ◽

Qiangqiang Yuan ◽

Li Yan

Keyword(s):

Remote Sensing ◽

Large Scale ◽

State Of The Art ◽

Remote Sensing Image ◽

Remote Sensing Images ◽

Image Dehazing ◽

Complex Issue ◽

Atmospheric Scattering ◽

Decoder Architecture ◽

Density Map

Remote sensing image dehazing is an extremely complex issue due to the irregular and non-uniform distribution of haze. In this paper, a prior-based dense attentive dehazing network (DADN) is proposed for single remote sensing image haze removal. The proposed network, which is constructed based on dense blocks and attention blocks, contains an encoder-decoder architecture, which enables it to directly learn the mapping between the input images and the corresponding haze-free image, without being dependent on the traditional atmospheric scattering model (ASM). To better handle non-uniform hazy remote sensing images, we propose to combine a haze density prior with deep learning, where an initial haze density map (HDM) is firstly extracted from the original hazy image, and is subsequently utilized as the input of the network, together with the original hazy image. Meanwhile, a large-scale hazy remote sensing dataset is created for training and testing of the proposed method, which contains both uniform and non-uniform, synthetic and real hazy remote sensing images. Experimental results on the created dataset illustrate that the developed dehazing method obtains significant progresses over the state-of-the-art methods.

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Local Deep Descriptor for Remote Sensing Image Feature Matching

Remote Sensing ◽

10.3390/rs11040430 ◽

2019 ◽

Vol 11 (4) ◽

pp. 430 ◽

Cited By ~ 4

Author(s):

Yunyun Dong ◽

Weili Jiao ◽

Tengfei Long ◽

Lanfa Liu ◽

Guojin He ◽

...

Keyword(s):

Remote Sensing ◽

Computer Vision ◽

Deep Learning ◽

Feature Matching ◽

Remote Sensing Image ◽

Image Feature ◽

Training Dataset ◽

Feature Descriptor ◽

Remote Sensing Images

Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

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Color Attenuation Prior (CAP) for Single Image Dehazing

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v7i2.10 ◽

2018 ◽

Vol 7 (02) ◽

pp. 23578-23584

Author(s):

Miss. Anjana Navale ◽

Prof. Namdev Sawant ◽

Prof. Umaji Bagal

Keyword(s):

State Of The Art ◽

Depth Map ◽

Depth Information ◽

Single Image ◽

Image Dehazing ◽

Atmospheric Scattering ◽

Haze Removal ◽

Ill Posed ◽

Single Input ◽

Scene Depth

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we have used a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.

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Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

Remote Sensing ◽

10.3390/rs13214443 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4443

Author(s):

Bo Jiang ◽

Guanting Chen ◽

Jinshuai Wang ◽

Hang Ma ◽

Lin Wang ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Wavelet Transform ◽

Large Scale ◽

Remote Sensing Image ◽

Superior Performance ◽

Learning Technology ◽

Remote Sensing Images ◽

Learning Methods ◽

Atmospheric Scattering

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

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MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3454009 ◽

2021 ◽

Vol 17 (2s) ◽

pp. 1-11

Author(s):

Xiaochuan Tang ◽

Mingzhe Liu ◽

Hao Zhong ◽

Yuanzhen Ju ◽

Weile Li ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Large Scale ◽

Remote Sensing Image ◽

Disaster Risk ◽

Multiple Instance Learning ◽

Remote Sensing Images ◽

Loess Area ◽

Remote Sensing Image Classification ◽

Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

Remote Sensing ◽

10.3390/rs13050869 ◽

2021 ◽

Vol 13 (5) ◽

pp. 869

Author(s):

Zheng Zhuo ◽

Zhong Zhou

Keyword(s):

Remote Sensing ◽

Image Retrieval ◽

State Of The Art ◽

Remote Sensing Image ◽

Storage Space ◽

Remote Sensing Images ◽

Retrieval Method ◽

Organization Management ◽

Deep Feature ◽

Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

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Research on Remote Sensing Image Matching with Special Texture Background

Symmetry ◽

10.3390/sym13081380 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1380

Author(s):

Sen Wang ◽

Xiaoming Sun ◽

Pengfei Liu ◽

Kaige Xu ◽

Weifeng Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Image ◽

Geometric Distortion ◽

Reference Image ◽

Remote Sensing Images ◽

Scale Invariant ◽

Sift Algorithm ◽

Speeded Up Robust Features ◽

Adaptive Quantization ◽

Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

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A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs13040747 ◽

2021 ◽

Vol 13 (4) ◽

pp. 747

Author(s):

Yanghua Di ◽

Zhiguo Jiang ◽

Haopeng Zhang

Keyword(s):

Remote Sensing ◽

Image Data ◽

Remote Sensing Image ◽

Google Earth ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Visual Categorization ◽

Class Differences ◽

Fine Grained ◽

Ship Classification

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

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Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

Remote Sensing ◽

10.3390/rs10121934 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1934 ◽

Cited By ~ 11

Author(s):

Bao-Di Liu ◽

Wen-Yang Xie ◽

Jie Meng ◽

Ye Li ◽

Yanjiang Wang

Keyword(s):

Remote Sensing ◽

Test Sample ◽

Remote Sensing Image ◽

Image Features ◽

Superior Performance ◽

Collaborative Representation ◽

Great Success ◽

Specific Class ◽

Remote Sensing Images ◽

Training Samples

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

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Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽

10.3390/s18103232 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3232 ◽

Cited By ~ 17

Author(s):

Yan Liu ◽

Qirui Ren ◽

Jiahui Geng ◽

Meng Ding ◽

Jiangyun Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Training Data ◽

Land Resources ◽

Remote Sensing Images ◽

Training Strategy ◽

The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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