A novel feature descriptor for automatic change detection in remote sensing images

Dalmiya, C. P.; Santhi, N.; Sathyabama, B.

doi:10.1016/j.ejrs.2018.03.005

Cited by 11 publications

(7 citation statements)

References 35 publications

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“…Change detection involves the use of multi-temporal datasets to discriminate areas of land cover change between dates of imaging. It is widely used in the application of remote sensing that examines multi-temporal datasets (Dalmiya et al 2019;Stehman and Foody 2019). Othow et al (2017) used remote sensing change detection techniques to analyze the rate of LULCC with special emphasis on forest cover change in Gog district of Gambella Regional State in Ethiopia.…”

Section: Forest Cover Change Detection Techniquementioning

confidence: 99%

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

2020

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Background: Forest plays an important role in climate regulation and carbon sequestration. Komto Forest is one of the remnant natural forests found in Guto Gida district of East Wollega zone, Ethiopia, has been supporting the local community for construction, energy and household furniture. Currently, influenced by land use land cover change (LULCC), this forest has been declining at an alarming rate. Detecting LULCC and understanding the driving forces has important for supporting decision making processes. We examine variation in forest cover dynamics over the period 1991-2012 using Landsat TM image of 1991, ETM + of 2002 and OLI-TIRS of 2019. Results: The LULCC detection results show that a dramatic increase of agricultural land from (24.78%) in 1991 to (33.5%) in 2019 with annual expansion rate (23.68%) per annum, where forest cover declined by 20.1% in 1991 and 37.38% in 2019 with annual decreasing rate of 4.18% per annum. Our finding indicates the increment of agricultural land, grassland, and settlement, while the dense and open forest cover shows a declining trend. The declining of forest coverage is likely to cause unpleasant environment and affects human wellbeing. Conclusions: The massive declined in forest cover change are often associated with agricultural expansion in the periphery of the forest. Timber exploitation and charcoal production are other problems that contribute for the declining of forest coverage. Overall, our results suggest the need of participatory forest management and public awareness creation to sustain the Komto remnant forest.

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Section: Forest Cover Change Detection Techniquementioning

confidence: 99%

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

2020

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“…The change detection approach is based on object-oriented features which are calculated from two classes of clustering by K-means algorithms, genetic K algorithms, and Self Organizing Map (SOM) clusters. Dalmiya et al [26] have proposed the Structural Phase Congruency Histogram (SPCH) which describes the detection of automatic changes without significant loss of information. The yield of a particular characteristic depends on the structural characteristics of the image, which differ in visual intensity and brightness.…”

Section: Related Workmentioning

confidence: 99%

Efficient Urban Green Space Destruction and Crop Stress Yield Assessment Model

Chamundeeswari¹,

Srinivasan²,

Bharathi³

2022

Intelligent Automation &Amp; Soft Computing

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Remote sensing (RS) is a very reliable and effective way to monitor the environment and landscape changes. In today's world topographic maps are very important in science, research, planning and management. It is quite possible to detect the changes based on RS data which is obtained at two different times. In this paper, we propose an optimal technique that handles problems like urban green space destruction and detection of crop stress assessment. Firstly, the optimal preprocessing is performed on the given RS dataset, for image enhancement using geometric correction and image registration. Secondly, we propose the improved cat swarm optimization algorithm to optimize the greenery region with the help of vegetation index parameters like Normalized Difference Built-up Index (NDBI) & Normalized Difference Vegetation Index (NDVI). Thirdly, we use Conditional Principal Component Analysis (PCA) to reduce dimension of a response matrix & retain the dominant information to identify key vegetation indices and the classification of crops. Then, an optimal decision maker-based post classification method is introduced to differentiate area changes based on the overlay of two or more classified images. From the simulation results we observed and conclude that the performance of proposed crop classification, crop stress and yield assessments performed very effective compared to existing methods in terms of F-Measure, recall, precision & accuracy.

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“…The GS transformation is not limited by the number of bands and can better save image information, but its anti-interference ability is not great, it takes a long time. The Rochester Institute of Technology (RIT) has proposed the Nearest Neighbor Diffusion Pan Sharpening (NNDiffuse) [26]- [30]. The advantage of this algorithm is that it better maintains the spectral information of the image and maintains the color [31]- [34].…”

Section: Introductionmentioning

confidence: 99%

Change Detection Algorithm for Multi-Temporal Remote Sensing Images Based on Adaptive Parameter Estimation

Zhang

Menenti

2020

IEEE Access

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This paper proposes a multi-temporal image change detection algorithm based on adaptive parameter estimation, which is used to solve the problems of severe interference of coherent speckle noise and the retention of detailed information about changing regions in synthetic aperture radar remote sensing images. The change area in the initial differential image has local consistency and global prominence. By detecting the significant area to locate similar change areas, the coherent speckle noise outside the area can be eliminated. The use of hierarchical FCM clustering to automatically generate training samples can improve the reliability of training samples. In addition, in order to increase the distinction between the changed area and the non-changed area, a sparse automatic encoder is used to extract the changed features and generate a change detection map. Experiments using 4 sets of SAR images show that the algorithm can effectively reduce the effect of speckle noise on detection accuracy, the extraction of changing areas is more complete and meticulous, and the false detection rate is greatly reduced. Since the images in different time phases will be disturbed by weather, clouds, sea water, etc., the target segmentation algorithm can be used to extract the target of interest and highlight the changing area. Principal component analysis and k-means clustering method are used to reduce the influence of isolated pixels, and change information is extracted to obtain different images. The experiment uses four sets of image data of islands and reefs. The experiment proves that the algorithm can well eliminate external interference, improve the accuracy of change detection, and have a good detection effect on the area of islands and reefs. The adaptive parameter estimation plays a good role in the detection of changing areas, and the visual effect is better, which can improve the accuracy of the detection results. INDEX TERMS Adaptive parameters, multi-temporal, remote sensing images, change detection model, superficial superposition.

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A novel feature descriptor for automatic change detection in remote sensing images

Cited by 11 publications

References 35 publications

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia

Efficient Urban Green Space Destruction and Crop Stress Yield Assessment Model

Change Detection Algorithm for Multi-Temporal Remote Sensing Images Based on Adaptive Parameter Estimation

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