Segmentation and Classification Using Logistic Regression in Remote Sensing Imagery

Khurshid, Hasnat; Khan, Muhammad Faisal

doi:10.1109/jstars.2014.2362769

Cited by 23 publications

(9 citation statements)

References 6 publications

(3 reference statements)

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“…The random errors of the classification process are characterized by sensitivity, specificity, and accuracy [10,43] which are calculated in Table 7, where: TP is the number of true positive cases, TN is the number of true negative cases, FP is the number of false positive cases, and FN is the number of false negative cases. In [12] an accuracy of 87% is obtained using RGB information and six texture features (fixed) extracted from gray level co-occurrence matrix.…”

Section: Resultsmentioning

confidence: 99%

“…In order to detect the flood by image analysis, three solutions usually appear in the literature: (a) use of images from satellites [9,10,11]; (b) use of images from fixed cameras on the ground [4,12,13]; and (c) use of images from aircrafts or UAVs [14]. …”

Section: Introductionmentioning

confidence: 99%

“…Based on this classification and on images taken at pre-flood and post-flood time instants, information about the flooding hazard is provided. In general the satellite applications for flood detection, like those presented in [10] from Spot-5 imagery, or in [15] by WorldView-2 satellite imagery, are based on high spatial resolution images and have the disadvantage of being high-cost solutions, hence less approachable for public use. In addition, these solutions have the disadvantage of being sensitive to weather patterns (clouds or other obscuring weather features will render them useless).…”

Section: Introductionmentioning

confidence: 99%

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Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Popescu

Ichim

Stoican

2017

Sensors

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Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Popescu

Ichim

Stoican

2017

Sensors

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“…We used ArcGIS software to create 100 random sampling points and then searched Google Images for images corresponding to these points, in order to determine whether each plot had been abandoned. Recall was calculated as follows [42]:…”

Section: Accuracy Evaluation Of Abandoned Croplandmentioning

confidence: 99%

Mapping Abandoned Cropland Changes in the Hilly and Gully Region of the Loess Plateau in China

Wang

Song

2021

Land

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As a form of land marginalization, abandoned cropland has an important impact on food security and the effective supply of agricultural products. With rapid urbanization across the world, large areas of cropland are abandoned in some regions, especially in mountainous and hilly areas with poor terrain. Due to the fine fragmentation and scattered distribution of abandoned cropland, and considering differences in the abandoned and fallow time of cropland, it is difficult to extract information using remote sensing technology. Therefore, this paper proposes a change in the detection method for extracting abandoned cropland information based on identifying the annual land use trajectory. Based on Landsat satellite data, annual land use was mapped from 2011 to 2020 in Gaolan County, which is located in the hilly and gully region of the Loess Plateau of China, using the random forest classification method. Subsequently, abandoned cropland information in Gaolan County was extracted, based on the land use change trajectory and analysis of the influencing factors of abandoned land. The results showed that: (1) The overall accuracy of land use interpretation in Gaolan County ranged from 86.44% to 95.45%, from 2011 to 2020, with a kappa coefficient of up to 0.93, and the classification results were ideal. (2) The recall of extracted abandoned cropland was 81%, the extraction accuracy of which was relatively high. (3) From 2013 to 2020, the cropland abandonment rate in Gaolan County ranged from 8.41% to 19.65%, with an average of 14.55%, which increased and then decreased. The abandonment rate was highest in 2015 but it then decreased year by year. The average period of abandoned cropland was 4.2 years. (4) The influence factors of the plot scale explain the difference in the spatial distribution of cultivated land abandonment. The higher the slope condition, the lower the soil nutrient content and the greater the possibility of abandonment.

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“…In our work, we employ an MLR on hand-crafted features derived from both LiDAR data and optical imagery. MLR has been used in numerous remote sensing classification tasks, 40 and it can produce multiclass probabilistic estimates. More importantly, MLR is fully probabilistic, therefore it provides calibrated probabilities off-the-shelf, whereas SVM and random-forest require postprocessing to compute multiclass (e.g., one versus all) and probabilities (Platt method 41 using cross validation).…”

Section: Multinomial Logistic Regressionmentioning

confidence: 99%

Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields

Liu

Piramanayagam

Monteiro

et al. 2019

J. Appl. Rem. Sens.

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Aerial images acquired by multiple sensors provide comprehensive and diverse information of materials and objects within a surveyed area. The current use of pretrained deep convolutional neural networks (DCNNs) is usually constrained to three-band images (i.e., RGB) obtained from a single optical sensor. Additional spectral bands from a multiple sensor setup introduce challenges for the use of DCNN. We fuse the RGB feature information obtained from a deep learning framework with light detection and ranging (LiDAR) features to obtain semantic labeling. Specifically, we propose a decision-level multisensor fusion technique for semantic labeling of the very-high-resolution optical imagery and LiDAR data. Our approach first obtains initial probabilistic predictions from two different sources: one from a pretrained neural network fine-tuned on a three-band optical image, and another from a probabilistic classifier trained on LiDAR data. These two predictions are then combined as the unary potential using a higher-order conditional random field (CRF) framework, which resolves fusion ambiguities by exploiting the spatial-contextual information. We utilize graph cut to efficiently infer the final semantic labeling for our proposed higher-order CRF framework. Experiments performed on three benchmarking multisensor datasets demonstrate the performance advantages of our proposed method. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Segmentation and Classification Using Logistic Regression in Remote Sensing Imagery

Cited by 23 publications

References 6 publications

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

Mapping Abandoned Cropland Changes in the Hilly and Gully Region of the Loess Plateau in China

Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields

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