Log-Based Transformation Feature Learning for Change Detection in Heterogeneous Images

Zhan, Tao; Gong, Maoguo; Jiang, Xiangming; Li, Shuwei

doi:10.1109/lgrs.2018.2843385

Cited by 79 publications

(46 citation statements)

References 18 publications

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“…For example, one may want to detect changes between an earlier SAR image and a later optical image, and vice versa, for the same area. Some representative papers [62][63][64][65][66][67] have been published in recent years. This research is very challenging, and more research is needed.…”

Section: Other Approachesmentioning

confidence: 99%

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Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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Section: Other Approachesmentioning

confidence: 99%

“…As mentioned earlier, there are some recent advances [63][64][65][66][67]152,153] in change detection using multimodal or heterogeneous images. However, more research is still needed to yield consistent results.…”

Section: Possibility Of Change Detection Using Multimodal Imagesmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

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“…Different from homogeneous CD, the pixels in heterogeneous images are in different distinct feature spaces [7], and the change map (CM) cannot be obtained by simple linear operations or some homogeneous methods, which is also the main difficulty for heterogeneous CD. Over the past several decades, much attention has been paid to homogeneous CD [8], and many excellent methods have been explored [9][10][11][12][13]. With the increase of different types of satellite sensors, however, CD based on homogeneous images is far away from the practical demands [8] especially when the homogeneous images are not available.…”

Section: Introductionmentioning

confidence: 99%

“…Over the past several decades, much attention has been paid to homogeneous CD [8], and many excellent methods have been explored [9][10][11][12][13]. With the increase of different types of satellite sensors, however, CD based on homogeneous images is far away from the practical demands [8] especially when the homogeneous images are not available. For example, when an optical image of a given area is provided by available remote sensing image archive data and only a new SAR image can be acquired (for technical reasons, lack of time, availability, or atmospheric conditions) in an emergency for the same area [14], we can only use the heterogeneous images to detect changes.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, one can also transfer the first image from its original feature space to the feature space where the second image is. For example, Zhan et al [8] proposed a CD method for heterogeneous images based on the logarithmic transformation feature learning framework, which first transfers the SAR image to the feature space where the optical image is so that they have similar statistical distribution properties, then uses a stacked denoising auto-encoder (SDAE) to get the final CM. Niu et al [27] adopted a conditional generative adversarial network (cGAN) to translate the optical image into the SAR feature space and directly compared the translated image with the approximated SAR image to get the final CM.…”

Section: Introductionmentioning

confidence: 99%

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A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

et al. 2020

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With the development of remote sensing technologies, change detection in heterogeneous images becomes much more necessary and significant. The main difficulty lies in how to make input heterogeneous images comparable so that the changes can be detected. In this paper, we propose an end-to-end heterogeneous change detection method based on the feature space constraint. First, considering that the input heterogeneous images are in two distinct feature spaces, two encoders with the same structure are used to extract features, respectively. A decoder is used to obtain the change map from the extracted features. Then, the Gram matrices, which include the correlations between features, are calculated to represent different feature spaces, respectively. The squared Euclidean distance between Gram matrices, termed as feature space loss, is used to constrain the extracted features. After that, a combined loss function consisting of the binary cross entropy loss and feature space loss is designed for training the model. Finally, the change detection results between heterogeneous images can be obtained when the model is trained well. The proposed method can constrain the features of two heterogeneous images to the same feature space while keeping their unique features so that the comparability between features can be enhanced and better detection results can be achieved. Experiments on two heterogeneous image datasets consisting of optical and SAR images demonstrate the effectiveness and superiority of the proposed method.

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Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification

Liu

Cao

et al. 2020

J Digit Imaging

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Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. ( 3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results.

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Log-Based Transformation Feature Learning for Change Detection in Heterogeneous Images

Cited by 79 publications

References 18 publications

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification

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