DeepSat V2: feature augmented convolutional neural nets for satellite image classification

Li, Qun; Basu, Supratim; Ganguly, Sangram; Mukhopadhyay, Supratik; DiBiano, Robert; Manohar, Krishpersad; Nemani, Ramakrishna R.

doi:10.1080/2150704x.2019.1693071

Cited by 48 publications

(28 citation statements)

References 21 publications

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“…In the literature, it is common to see precisions higher than 90% [31,44] with several classes. What could explain that our best model reached 77% with two classes?…”

Section: Discussionmentioning

confidence: 99%

“…[43] express how important the context could be to classify a tile extracted from high resolution satellite imagery. This is also discussed in [44] and [31]. The authors explain that a tile should be large enough to give a context to the target, but small enough to not have different objects in it.…”

Section: Methodsmentioning

confidence: 99%

“…Thanks to CNNs, companies have developed powerful applications in fields such as image recognition [25,26], object detection [27,28], semantic segmentation [27,29] amongst others. In remote sensing applications, CNNs has proved to be efficient to identify different object classes (e.g., roads, buildings, forest, or water bodies) or to segment those within images [30,31].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

et al. 2020

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Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada’s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.

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“…In the literature, it is common to see precisions higher than 90% [31,44] with several classes. What could explain that our best model reached 77% with two classes?…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

et al. 2020

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“…Li et al [14] proposed the feature fusion single shot multibox detector (FSSD) model, which reconstructs the multi-scale features of the model through feature fusion and down-sampling operation, and enriches the feature details to improve the detection performance on small objects. Liu et al [15] proposed the DeepSat classification framework based on the ''hand-made'' features and deep belief network (DBN). The framework augments a CNN with handcrafted features (instead of using DBN-based architecture) for classification.…”

Section: Introductionmentioning

confidence: 99%

Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images

Zhang

et al. 2020

IEEE Access

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Noting the shortcomings of current methods in detecting small objects in image-based remote sensing applications, in this paper, we propose a novel implementation of single shot multibox detector (SSD) networks based on dilated convolution and feature fusion. We call this algorithm dilated convolution and feature fusion single shot multibox detector (DFSSD). This algorithm removes the random clipping steps of data preprocessing layers in conventional SSD networks and utilizes the structure of feature pyramid network (FPN) network to fuse the low-level feature map with high resolution and the high-level feature map with rich semantic information. It also enhances the receptive field of the third-level feature map of the DFSSD network by using dilated convolution. In the data processing step of the model, we use the image segmentation of the feature point region proposals to improve the training sample size. The mean average precision (mAP) value of the proposed DFSSD network, when tested on remote sensing datasets, achieves 76.51%, which is significantly higher than that of the SSD model (69.81%).

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“…Although the above methods based on deep learning greatly enhance the accuracy and efficiency of remote sensing image segmentation [ 24 , 25 , 26 ], a robust model usually requires relevant experts to spend a lot of time and energy to complete it. Feature extraction and fusion are key for robust and effective image processing in remote sensing [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

NAS-HRIS: Automatic Design and Architecture Search of Neural Network for Semantic Segmentation in Remote Sensing Images

Zhang

Jing

Lin

et al. 2020

Sensors

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The segmentation of high-resolution (HR) remote sensing images is very important in modern society, especially in the fields of industry, agriculture and urban modelling. Through the neural network, the machine can effectively and accurately extract the surface feature information. However, using the traditional deep learning methods requires plentiful efforts in order to find a robust architecture. In this paper, we introduce a neural network architecture search (NAS) method, called NAS-HRIS, which can automatically search neural network architecture on the dataset. The proposed method embeds a directed acyclic graph (DAG) into the search space and designs the differentiable searching process, which enables it to learn an end-to-end searching rule by using gradient descent optimization. It uses the Gumbel-Max trick to provide an efficient way when drawing samples from a non-continuous probability distribution, and it improves the efficiency of searching and reduces the memory consumption. Compared with other NAS, NAS-HRIS consumes less GPU memory without reducing the accuracy, which corresponds to a large amount of HR remote sensing imagery data. We have carried out experiments on the WHUBuilding dataset and achieved 90.44% MIoU. In order to fully demonstrate the feasibility of the method, we made a new urban Beijing Building dataset, and conducted experiments on satellite images and non-single source images, achieving better results than SegNet, U-Net and Deeplab v3+ models, while the computational complexity of our network architecture is much smaller.

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DeepSat V2: feature augmented convolutional neural nets for satellite image classification

Cited by 48 publications

References 21 publications

A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

A Deep Learning Approach to the Detection of Gossans in the Canadian Arctic

Dilated Convolution and Feature Fusion SSD Network for Small Object Detection in Remote Sensing Images

NAS-HRIS: Automatic Design and Architecture Search of Neural Network for Semantic Segmentation in Remote Sensing Images

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