Cross-Domain CNN for Hyperspectral Image Classification

Lee, Hyungtae; Eum, Sungmin; Kwon, Heesung

doi:10.1109/igarss.2018.8519419

Cited by 20 publications

(12 citation statements)

References 9 publications

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“…With the continuous improvement of many researchers, deeper neural networks have been proposed, such as AlexNet [17] and VGGNet [18]. Lee and Kwon [19] and Lee et al [20] developed a cross-domain convolutional neural network for a variety of hyperspectral images. After three sets of experiments, the results improved accuracy by 1% to 3% compared to the method using independent convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Object Detection Improvement for Tomato Disease

2020

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To improve the recognition model accuracy of crop disease leaves and locating diseased leaves, this paper proposes an improved Faster RCNN to detect healthy tomato leaves and four diseases: powdery mildew, blight, leaf mold fungus and ToMV. First, we use a depth residual network to replace VGG16 for image feature extraction so we can obtain deeper disease features. Second, the k-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, we carry out a k-means experiment with three kinds of different feature extraction networks. The experimental results show that the improved method for crop leaf disease detection had 2.71% higher recognition accuracy and a faster detection speed than the original Faster RCNN.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Object Detection Improvement for Tomato Disease

2020

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“…where H ×W represents the height and width of input x to the network, respectively, c is the number of classes, and y i,k andŷ i,k are the ground-truth and predicted values for the ith pixel x i and kth class among C different possible classes, respectively [30]. The combined weighted loss, L c , is defined as follows:…”

Section: Proposed Combined U-net Modelmentioning

confidence: 99%

Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets

Song

Kim

2020

IJGI

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Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To address this problem, we propose a combined U-net model that is trained using a combined weighted loss function and can handle heterogeneous datasets. The network consists of encoder and decoder blocks. The convolutional layers that form the encoder blocks are shared with the heterogeneous datasets, and the decoder blocks are assigned separate training weights. Herein, the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image datasets, respectively. When the layers are shared, only visible bands of the ISPRS Potsdam data are used. Experimental results show that when same-sized heterogeneous datasets are used, the semantic segmentation accuracy of the Potsdam data obtained using our proposed method is lower than that obtained using only the Potsdam data (four bands) with other methods, such as SegNet, DeepLab-V3+, and the simplified version of U-net. However, the segmentation accuracy of the Potsdam images is improved when the larger Cityscape dataset is used. The combined U-net model can effectively train heterogeneous datasets and overcome the insufficient training data problem in the context of RS-image datasets. Furthermore, it is expected that the proposed method can not only be applied to segmentation tasks of aerial images but also to tasks with various purposes of using big heterogeneous datasets.

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“…In this work, the word ''semi-supervised" refers to the use of external images (from other image domains) to generate impostor patch-pairs. The proposed training scenario is inspired by some similar works on cross-domain adaptation and transfer learning that have been applied in many research works [19], [20], [21]. Therefore, our method requires nothing more than knowing the type of change to be addressed, so that the substituting images will be selected accordingly.…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Unlabeled Optical Remote Sensing Data Using Siamese CNN

Hedjam

Abdesselam

Melgani

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, we propose a new semi-supervised method to detect the changes occurring in a geographical area after a major damage. We detect the changes by processing a pair of optical remote sensing images. The proposed method adopts a patch-based approach, whereby we use a Siamese CNN (S-CNN), trained with augmented data, to compare successive pairs of patches obtained from the input images. The main contribution of this work lies in developing a S-CNN training phase without resorting to class labels that are actually not available from the input images. We train the S-CNN using genuine and impostor patch-pairs defined in a semi-supervised way from the input images. We tested the proposed change detection model on four real datasets and compared its performance to those of two existing models. Obtained results were very promising. Index Terms-remote sensing change detection. Siamese CNN. Semi-supervised change detection.

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Cross-Domain CNN for Hyperspectral Image Classification

Cited by 20 publications

References 9 publications

Deep Learning-Based Object Detection Improvement for Tomato Disease

Deep Learning-Based Object Detection Improvement for Tomato Disease

Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets

Change Detection in Unlabeled Optical Remote Sensing Data Using Siamese CNN

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