Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Guirado, Emilio; Tabik, Siham; Alcaraz‐Segura, Domingo; Cabello, Javier; Herrera, Francisco

doi:10.3390/rs9121220

Cited by 150 publications

(130 citation statements)

References 52 publications

(67 reference statements)

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“…Certainly, only the CNN can overcome the fact that the complexity of VHR images can cause traditional human-dependent classification models to fail due to the limited representation power of handcrafted features, 34 thereby obtaining class information from complex image blocks. It can be concluded that the proposed BOBIC method was successful at applying the CNN to OBIC, which also proves the hypothesis of Guirado et al 33 that stated that the inclusion of CNN-models could further improve OBIA methods.…”

Section: Discussionsupporting

confidence: 68%

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Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery

Johnson³

2018

J. Appl. Rem. Sens.

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, "Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery," J. Appl. Remote Sens. 12(2), 025010 (2018), doi: 10.1117/1.JRS.12.025010. Abstract. Convolutional neural network (CNN) has shown great success in computer vision tasks, but their application in land-use type classifications within the context of object-based image analysis has been rarely explored, especially in terms of the identification of irregular segmentation objects. Thus, a blocks-based object-based image classification (BOBIC) method was proposed to carry out end-to-end classification for segmentation objects using CNN. Specifically, BOBIC takes advantage of CNN to automatically extract complex features from the original image data, thereby avoiding the uncertainty caused by the manual extraction of features in OBIC. Additionally, OBIC compensates for the shortcomings of CNN whereby it is difficult to delineate a clear right boundary for ground objects at the pixel level. Using three high-resolution test images, the proposed BOBIC was compared with support vector machine (SVM) and random forest (RF) classifiers, and then, the effect of image blocks and mixed objects on classification accuracy was evaluated for the proposed BOBIC. Compared with conventional SVM and RF classifiers, the inclusion of CNN improved the OBIC classification performance substantially (5% to 10% increases in overall accuracy), and it also alleviated the effect derived from mixed objects.

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Section: Discussionsupporting

confidence: 68%

“…Guirado et al 33 compared state-of-the-art OBIA methods with CNNbased methods for the detection of plant species of conservation concern and reasoned that adopting the CNN-based methods could further improve OBIA methods. Zhao et al 34 proposed a two-step OBIC framework using a combination of handcrafted and deep CNN features.…”

Section: Introductionmentioning

confidence: 99%

Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery

Johnson³

2018

J. Appl. Rem. Sens.

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“…Future work must investigate the diversity of backgrounds and the size of image classes needed for model training. Similar tools are developing for screening camera trap images (Chen, Han, He, Kays, & Forrester, 2014), and finding organisms in remotely sensed imagery (Guirado, Tabik, Alcaraz-Segura, Cabello, & Herrera, 2017). Convolutional neural networks underlie many advances in these areas, and connecting insights across disciplines will be a key in developing best practices.…”

Section: Discussionmentioning

confidence: 99%

Scene‐specific convolutional neural networks for video‐based biodiversity detection

Weinstein

2018

Methods Ecol Evol

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Finding, counting and identifying animals is a central challenge in ecology. Most studies are limited by the time and cost of fieldwork by human observers. To increase the spatial and temporal breadth of sampling, ecologists are adopting passive image‐based monitoring approaches. While passive monitoring can expand data collection, a remaining obstacle is finding the small proportion of images containing ecological objects among the majority of frames containing only background scenes. I proposed a scene‐specific convolutional neural network for detecting animals of interest within long duration time‐lapse videos. Convolutional neural networks are a type of deep learning algorithm that have recently made significant advances in image classification. The approach was tested on videos of floral visitation by hummingbirds. Despite low frame rates, poor image quality, and complex video conditions, the model correctly classified over 90% of frames containing hummingbirds. Combining motion detection and image classification can substantially reduce the time investment in scoring images from passive monitoring studies. These results underscore the promise of deep learning to lead ecology into greater automation using passive image analysis. To help facilitate future applications, I created a desktop executable that can be used to apply pre‐trained models to videos, as well as reproducible scripts for training new models on local and cloud environments.

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“…Google Earth images have only three bands: red, green, and blue (RGB). Owing to the poor spectral information, there have not been too many ISA or land-cover mapping studies based on Google Earth image, as they are mainly used for small object detection, such as village buildings [18], scattered shrubs [19], ships [20], and qanat shafts [21]. As a free high spatial resolution image data source, Google Earth has a high potential for high resolution ISA mapping.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence techniques, such as deep learning, have shown a high performance of vegetation detection in high-resolution images [19]. Other geometric and textural features can also help reduce the confusion between ISA and soil.…”

mentioning

confidence: 99%

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

Huang

Chen

et al. 2018

Remote Sensing

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Implying the prosperity and development of the city, impervious surface area (ISA) is playing an increasingly important role in ecological processes, microclimate, material and energy flows, and urban flood. The free sub-meter resolution Google Earth image, which is integrated by several high spatial resolution data, appears to have potential for high-resolution ISA extraction, where present study is rare and performances remain to be improved. Due to the high spatial and spectral variation of the urban environment as well as confusion between ISA and soil, the accurate delineating of ISA with traditional (direct) methods can be costly and time-consuming, which is in a word resource-intensive. However, this paper presents a novel indirect ISA extraction conceptual model and a new detecting and masking background land cover (DMBLC) approach that: uses a freely available, high-resolution dataset; requires a reduced set of training samples; and consists of relatively simple, common, and feasible image processing steps. The key characteristic of DMBLC is to detect the background of ISA (vegetation, soil, and water) accurately and obtain the ISA by masking the background. The approach relies on background detection to avoid the predicaments of direct ISA extraction. Water can be directly gained by water body vector data, in DMBLC; mixture tuned matched filtering (MTMF) is exploited to detect vegetation and soil, image segmentation is used to mitigate the spectral variation problem within the same land cover, and segment rectangularity reduces the confusion between ISA and soil. From experiments in a core area of Fuzhou, China, the DMBLC approach reached high performance and outperformed the powerful traditional support vector machines (SVM) method (overall accuracy of 94.45% and Kappa coefficient of 0.8885, compared to 86.44% and 0.7329, respectively). From the comparison of different levels of complexity within the inner processing steps, it is confirmed that the DMBLC approach is a powerful and flexible changed framework for indirect ISA extraction, which can be improved by using more advanced inner methods.

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Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study

Cited by 150 publications

References 52 publications

Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery

Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery

Scene‐specific convolutional neural networks for video‐based biodiversity detection

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

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