Computational intelligence in optical remote sensing image processing

Zhong, Yanfei; Ma, Ailong; Ong, Yew Soon; Zhu, Zexuan; Zhang, Liangpei

doi:10.1016/j.asoc.2017.11.045

Cited by 167 publications

(51 citation statements)

References 126 publications

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“…The DE algorithm was first proposed by Storn and Price [33], and it is a real value-encoded EA with promising global optimization capability. DE often performs well in all types of optimization problems without any assumption, and it has been proven effective in image clustering [23]. Zhong et al proposed a clustering method based on the adaptive multi-objective DE algorithm, which could achieve high accuracy [24].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…1 (22) where PCC represents the percentage of correct classification, PRE represents the proportion of expected agreement. PCC and PRE are defined by Equations (23) and (24).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Since EAs have promising optimization ability, which can jump out of the local minima, EAs-based optimal clustering has been applied to band selection [22], image classification [23][24][25], and change detection [8,20,21,[26][27][28][29][30][31][32]. Celik used the genetic algorithm (GA) to generate optimal CM with minimum mean square error (MSE) [20].…”

Section: Introductionmentioning

confidence: 99%

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Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

Song

Zhong

2018

Remote Sensing

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Change detection (CD) of natural land cover is important for environmental protection and to maintain an ecological balance. The Landsat series of satellites provide continuous observation of the Earth’s surface and is sensitive to reflection of water, soil and vegetation. It offers fine spatial resolutions (15–80 m) and short revisit times (16–18 days). Therefore, Landsat imagery is suitable for monitoring natural land cover changes. Clustering-based CD methods using evolutionary algorithms (EAs) can be applied to Landsat images to obtain optimal changed and unchanged clustering centers (clusters) with minimum clustering index. However, they directly analyze difference image (DI), which finds itself subject to interference by Gaussian noise and local brightness distortion in Landsat data, resulting in false alarms in detection results. In order to reduce image interferences and improve CD accuracy, we proposed an unsupervised CD method based on multi-feature clustering using the differential evolution algorithm (M-DECD) for Landsat Imagery. First, according to characteristics of Landsat data, a multi-feature space is constructed with three elements: Wiener de-noising, detail enhancement, and structural similarity. Then, a CD method based on differential evolution (DE) algorithm and fuzzy clustering is proposed to obtain global optimal clusters in the multi-feature space, and generate a binary change map (CM). In addition, the control parameters of the DE algorithm are adjusted to improve the robustness of M-DECD. The experimental results obtained with four Landsat datasets confirm the effectiveness of M-DECD. Compared with the results of conventional methods and the current state-of-the-art methods based on evolutionary clustering, the detection accuracies of the M-DECD on the Mexico dataset and the Sardinia dataset are very close to the best results. The accuracies of the M-DECD in the Alaska dataset and the large Canada dataset increased by about 3.3% and 11.9%, respectively. This indicates that multiple features are suitable for Landsat images and the DE algorithm is effective in searching for an optimal CD result.

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Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…1 (22) where PCC represents the percentage of correct classification, PRE represents the proportion of expected agreement. PCC and PRE are defined by Equations (23) and (24).…”

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

Song

Zhong

2018

Remote Sensing

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“…Hyperspectral imaging records the full spectral data (e.g., spectral reflectance) of the imaged sample, which can then be used for various tasks, such as improving the performance of image segmentation and classification tasks compared to the usual RGB and grayscale images (e.g., cancerous cell detection [4] and remote sensing [5]). Individual spectral band images of a hyperspectral image may also show improved visibility of features-of-interest which allows optimal wavelength band selection for designing feature-specific imaging systems (e.g., narrowband imaging [6,7] and burn depth assessment [8]).…”

Section: Introductionmentioning

confidence: 99%

Optical implementation of partially negative filters using a spectrally tunable light source, and its application to contrast enhanced oral and dental imaging

et al. 2019

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In optical imaging, optical filters can be used to enhance the visibility of featuresof-interest and thus aid in visualization. Optical filter design based on hyperspectral imaging employs various statistical methods to find an optimal design. Some methods, like principal component analysis, produce vectors that can be interpreted as filters that have a partially negative transmission spectrum. These filters, however, are not directly implementable optically. Earlier implementations of partially negative filters have concentrated on spectral reconstruction. Here we show a novel method for implementing partially negative optical filters for contrast-enhancement purposes in imaging applications. We describe the method and its requirements, and show its feasibility with color chart and dental imaging examples. The results are promising: visual comparison of computational color chart render and optical measurement show matching images, and visual inspection of dental images show increased contrast.

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“…Deep neural network (DNN) has been proven to be able to automatically learn a hierarchical feature representation, which is more robust to HSIs classification [22]- [24]. This type of feature is invariant to local changes and thus more suitable for handling the variable spectral/spatial signatures in HSIs [23].…”

mentioning

confidence: 99%

Active Transfer Learning Network: A Unified Deep Joint Spectral–Spatial Feature Learning Model for Hyperspectral Image Classification

Deng

Xue

Liu

et al. 2019

IEEE Trans. Geosci. Remote Sensing

143

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Deep learning has recently attracted significant attention in the field of hyperspectral images (HSIs) classification. However, the construction of an efficient deep neural network (DNN) mostly relies on a large number of labeled samples being available. To address this problem, this paper proposes a unified deep network, combined with active transfer learning that can be well-trained for HSIs classification using only minimally labeled training data. More specifically, deep joint spectral-spatial feature is first extracted through hierarchical stacked sparse autoencoder (SSAE) networks. Active transfer learning is then exploited to transfer the pre-trained SSAE network and the limited training samples from the source domain to the target domain, where the SSAE network is subsequently fine-tuned using the limited labeled samples selected from both source and target domain by corresponding active learning strategies. The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel active learning strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross-dataset and intra-image; 3) the learned deep joint spectral-spatial feature representation is more generic and robust than many joint spectral-spatial feature representation. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based methods, on three popular datasets.

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Computational intelligence in optical remote sensing image processing

Cited by 167 publications

References 126 publications

Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

Change Detection Based on Multi-Feature Clustering Using Differential Evolution for Landsat Imagery

Optical implementation of partially negative filters using a spectrally tunable light source, and its application to contrast enhanced oral and dental imaging

Active Transfer Learning Network: A Unified Deep Joint Spectral–Spatial Feature Learning Model for Hyperspectral Image Classification

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