Jit Mukherjee

Verified email at gmail.com

Assistant Professor, Dept. of CSE
Birla Institute of Technology



                                               

https://researchid.co/jitmukherjee

RESEARCH INTERESTS

Remote Sensing, Image Processing, Computer Vision, Machine Learning, Multimedia System

15

Scopus Publications

86

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Seasonal detection of coal overburden dump regions in unsupervised manner using landsat 8 OLI/TIRS images at jharia coal fields
    Jit Mukherjee, Jayanta Mukherjee, Debashish Chakravarty, and Subhash Aikat

    Multimedia Tools and Applications, ISSN: 13807501, eISSN: 15737721, Issue: 28-29, Pages: 35605-35627, Published: November 2021 Springer Science and Business Media LLC

  • A Study of Detecting Coal Seam Fires by Removing Other High Temperature Locations from Landsat 8 Oli/Tirs Images
    Jit Mukherjee, Jayanta Mukhopadhyay, Debashish Chakravarty, and Subhas Aikat

    International Geoscience and Remote Sensing Symposium (IGARSS), Pages: 4084-4087, Published: 26 September 2020 IEEE
    Coal seam fire has various environmental, social, and economical adversities. In the past, coal seam fire regions are detected by studying the land surface temperature properties of satellite images. Yet, these techniques do not consider the spectral properties of the fire locations. Thus, various other high temperature regions are falsely detected as coal seam fire regions. The objective of this paper is to detect coal seam fire regions by analyzing the land surface temperature and eliminating the falsely detected high temperature regions. We propose a novel technique for detecting coal seam fire regions by using clay mineral ratio, which can differentiate other high temperature regions from coal seam fire regions.

  • Automated Coastline Detection from Landsat 8 Oli/Tirs Images with the Presence of Inland Water Bodies in Andaman
    Rajdeep Mondal, Jit Mukherjee, and Jayanta Mukhopadhyay

    International Geoscience and Remote Sensing Symposium (IGARSS), Pages: 57-60, Published: 26 September 2020 IEEE
    Coastline detection, and monitoring have different research challenges. Global warming, deforestation, and sea level rising have several adverse effects on coastlines such as, erosion, coastline recession, loss of biodiversity, etc. In the past, various techniques have been proposed to detect, and monitor coastlines. While detecting coastlines, most of the techniques do not consider removing inland water bodies. The objective of this work is to remove inland water bodies, in the process of detection of coastlines. For accomplishing this task, we propose to use difference of Coal Mine Index (CMI) and Normalized Difference Water Index (NDWI) to separate water bodies from other regions. Subsequently, inland water bodies are identified from this set by computing and analyzing their contours, and removed to provide coastlines. Overall, this work presents a novel automated method of coastline detection with the presence of inland water bodies having precision, and recall of 80%, and 82.35%, respectively.

  • Automated Detection of Mine Water Bodies Using Landsat 8 OLI/TIRS in Jharia
    Jit Mukherjee, Jayanta Mukherjee, and Debashish Chakravarty

    Communications in Computer and Information Science, ISSN: 18650929, eISSN: 18650937, Volume: 1249, Pages: 480-489, Published: 2020 Springer Singapore

  • Automated Seasonal Detection of Coal Surface Mine Regions from Landsat 8 OLI Images
    Jit Mukherjee, Jayanta Mukhopadhyay, Debashish Chakravarty, and Subhas Aikat

    International Geoscience and Remote Sensing Symposium (IGARSS), Pages: 2435-2438, Published: July 2019 IEEE
    Detection, and monitoring of surface mining region have various research aspects. Coal surface mining has severe social, ecological, environmental adverse effects. In the past, semisupervised and supervised clustering techniques have been used to detect such regions. Coal has lower reflectance values in short wave infrared I (SWIR-I) than short wave infrared II (SWIR-II). The proposed method presents a novel approach to detect coal mine regions without manual intervention using this cue. Clay mineral ratio is defined as a ratio of SWIR-I to SWIR-II. Here, unsupervised K-Means clustering has been used in a hierarchical fashion over a variant of clay mineral ratio to detect opencast coal mine regions in the Jharia coal field (JCF), India. The proposed method has average precision, and recall of 76.43%, and 62.75%, respectively.

  • Automated seasonal separation of mine and non mine water bodies from landsat 8 oli/tirs using clay mineral and iron oxide ratio
    Jit Mukherjee, Jayanta Mukherjee, and Debashish Chakravarty

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN: 19391404, eISSN: 21511535, Pages: 2550-2556, Published: July 2019 Institute of Electrical and Electronics Engineers (IEEE)
    Opencast mining has huge effects on water pollution for several reasons. Fresh water is heavily used to process ore. Mine effluent and seepage from various mine related areas especially tailing reservoir, increase water pollution immensely. Monitoring and classification of mine water bodies, which have such environmental impacts, have several research challenges. In the past, land cover classification of a mining region detects mine and non mine water bodies simultaneously. Water bodies inside surface mines have different characteristics from other water bodies. In this paper, a novel method has been proposed to differentiate mine and non mine water bodies over the seasons, which does not require to set a threshold value manually. Here, water body regions are detected over the entire scene by any classical water body detection algorithm. Further, each water body is treated independently, and reflectance properties of a bounding box over each water body region are analyzed. In the past, there were efforts to use clay mineral ratio (CLM) to separate mine and non mine water bodies. In this paper, it has been observed that iron oxide ratio (IO) can also separate mine and non mine water bodies. The accuracy is observed to increase, if the difference of CLM and IO is used for segregation. The proposed algorithm separates these regions by taking into account seasonal variations. Means of differences of CLM and IO of each bounding box have been clustered using K-means clustering algorithm. The automation provides precision and recall for mine, and non mine water bodies as $[77.83\\%,76.55\\%]$ and $[75.18\\%,75.84\\%]$, respectively, using ground truths from high-definition Google Earth images.

  • A Novel Index to Detect Opencast Coal Mine Areas from Landsat 8 OLI/TIRS
    Jit Mukherjee, Jayanta Mukherjee, Debashish Chakravarty, and Subhas Aikat

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN: 19391404, eISSN: 21511535, Pages: 891-897, Published: March 2019 Institute of Electrical and Electronics Engineers (IEEE)
    Detection, classification, and monitoring of surface mining region from satellite images have vast challenges. Surface mining industry has huge effect on social, economical, ecological, and environmental welfare of a country. Machine learning techniques are used to capture the unique characteristics of surface mining region from satellite images. In opencast coal mines, raw coals stay exposed to the environment. Reflectance measures in certain wavelengths of such areas are distinguishable from other non-coal areas. These surface mining areas can be detected through satellite images using this cue. It has been found in this paper, that the spectral index derived from SWIR-I (1.566–1.651 <inline-formula><tex-math notation="LaTeX">$\\mu \\text{m}$</tex-math></inline-formula>) and SWIR-II (2.107–2.294 <inline-formula><tex-math notation="LaTeX">$\\mu \\text{m}$</tex-math></inline-formula>) bands of Landsat 8 exhibit distinctive features to detect high mineral areas of coal seams. Spectral plots of reflectance values of different land cover classes with respect to reflectance values of coal are found to be different. This paper presents a novel index to detect surface coal mines. Coal quarry and coal dump regions are considered as the true positive regions. The accuracy of the index has been validated with high-resolution Google Earth ground truth images and statistical measures. The robustness of the index is analyzed through seasonal variations. The average accuracy of the proposed method over the season is found to be <inline-formula><tex-math notation="LaTeX">${86.24\\%}$</tex-math></inline-formula>. The proposed index can be further used for classification and area monitoring of opencast coal mine regions.

  • Unsupervised Detection of Active, New, and Closed Coal Mines with Reclamation Activity from Landsat 8 OLI/TIRS Images
    Jit Mukherjee, Jayanta Mukherjee, Debashish Chakravarty, and Subhas Aikat

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 03029743, eISSN: 16113349, Volume: 11941 LNCS, Pages: 397-404, Published: 2019 Springer International Publishing

  • Investigation of seasonal separation in mine and non mine water bodies using local feature analysis of Landsat 8 OLI/TIRS images
    Jit Mukherjee, Jayanta Mukhopadhyay, and Debashish Chakravarty

    International Geoscience and Remote Sensing Symposium (IGARSS), Volume: 2018-July, Pages: 8961-8964, Published: 31 October 2018 IEEE
    Surface mining causes drastic land cover and land use changes and it has huge ecological, and environmental impacts. Restoration, reclamation, classification, and monitoring of surface mining region have several research aspects. In classification of surface mining, water bodies in mining regions are detected along with water bodies in non mining areas using various spectral information of satellite imagery. Water bodies in mining region have distinct characteristic. Detection of these distinct characteristics, and separation of mine, and non mine water body areas are the motivation of this paper. Normalized Difference Water Index (NDWI) is a widely practiced water detection technique which is used here. Detection of water bodies from NDWI may falsely detect few bare soil regions. Hence, in this work, bare soil regions are detected by Bare soil Index (BI) and further removed from the water map. The modified water map is analyzed by connected component computation and further mine, and non mine water bodies are separated by using the concept of clay mineral ratio.

  • Ontology-Driven content-based retrieval of heritage images
    Dipannita Podder, Jit Mukherjee, Shashaank Mattur Aswatha, Jayanta Mukherjee, and Shamik Sural

    Heritage Preservation: A Computational Approach, Pages: 143-160, Published: 15 June 2018 Springer Singapore

  • Detection of coal seam fires in summer seasons from landsat 8 OLI/TIRS in Dhanbad
    Jit Mukherjee, Jayanta Mukherjee, and Debashish Chakravarty

    Communications in Computer and Information Science, ISSN: 18650929, Volume: 841, Pages: 529-539, Published: 2018 Springer Singapore

  • Duplication detection for image sharing systems
    Jit Mukherjee, Shashaank M. Aswatha, Prasenjit Mondal, Jayanta Mukherjee, and Pabitra Mitra

    ACM International Conference Proceeding Series, Published: 14 December 2014 ACM
    Duplication of images is a common occurrence in community based data sharing systems. An image of the same scene, residing as multiple copies in the system, introduces redundancy. This paper describes a novel technique to detect such submissions by matching the Speeded Up Robust Features (SURF) of a query image to the feature set of images in the database, which are pre-computed, dimensionality reduced, and indexed. First, a set of similar images is obtained with their feature key-point correspondences by computing homography. An occurrence of duplication is verified by statistical hypothesis testing, which considers the distribution obtained by inter-key-point Euclidean distance ratios between the corresponding key-points among the query and candidate images.

  • A survey on image retrieval performance of different bag of visual words indexing techniques
    Jit Mukherjee, Jayanta Mukhopadhyay, and Pabitra Mitra

    IEEE TechSym 2014 - 2014 IEEE Students' Technology Symposium, Pages: 99-104, Published: 2014 IEEE
    In this paper a survey has been carried out over image retrieval performances of bag of visual words (BoVW) method using different indexing techniques. Bag of visual word method is a content based image retrieval technique, where images are represented as a sparse vector of occurrences of visual words. In this paper different indexing techniques are used to compute near similar visual word vectors of a query image. Locality sensitive hashing, SR-tree based indexing and naive L1 and L2 norm based distance metric calculation are used here. Standard datasets like, UKBench [19], holiday dataset [9] and images from SMARAK1 are used for performance analysis.

  • VIMARSHAK - A web based subjective image evaluation system
    Gazal Garg, Prasenjit Mondal, Shashaank M. Aswatha, Jit Mukherjee, Tapas Maji, and Jayanta Mukherjee

    Proceedings - 2014 5th International Conference on Signal and Image Processing, ICSIP 2014, Pages: 73-76, Published: 2014 IEEE
    In this paper, a secured web based online subjective image evaluation system has been proposed to assess different image processing algorithms. Since many image processing algorithms are designed to enhance the human perception of available image cues, subjective evaluation plays an important role in the assessment of the same. The proposed technique assesses several similar processes by accumulation of votes by individual human evaluators through pair wise comparisons of their outputs. Three tournament strategies are used for the pair wise image comparisons, namely knockout, challenging, and round-robin. The experiments show a satisfactory result in evaluation of accumulated ensemble of evaluators' votes, which is validated using Berkeley boundary detection benchmark dataset.

  • Real-time retrieval system for heritage images
    Sumit Mishra, Jit Mukherjee, Prasenjit Mondal, Shashaank M. Aswatha, and Jayanta Mukherjee

    Lecture Notes in Electrical Engineering, ISSN: 18761100, eISSN: 18761119, Volume: 248 LNEE, Pages: 245-253, Published: 2014 Springer India

RECENT SCHOLAR PUBLICATIONS

  • Seasonal detection of coal overburden dump regions in unsupervised manner using landsat 8 OLI/TIRS images at jharia coal fields
    J Mukherjee, J Mukherjee, D Chakravarty, S Aikat
    Multimedia Tools and Applications 80 (28), 35605-35627 2021

  • Land Cover Analysis of Surface Coal Mine Regions using Multi-Spectral Images
    J Mukherjee
    IIT Kharagpur 2020

  • Automated Coastline Detection from Landsat 8 Oli/Tirs Images with the Presence of Inland Water Bodies in Andaman
    R Mondal, J Mukherjee, J Mukhopadhyay
    IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium 2020

  • A Study of Detecting Coal Seam Fires by Removing Other High Temperature Locations from Landsat 8 Oli/Tirs Images
    J Mukherjee, J Mukhopadhyay, D Chakravarty, S Aikat
    IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium 2020

  • Automated Detection of Mine Water Bodies Using Landsat 8 OLI/TIRS in Jharia
    J Mukherjee, J Mukherjee, D Chakravarty
    National Conference on Computer Vision, Pattern Recognition, Image 2019

  • Unsupervised detection of active, new, and closed coal mines with reclamation activity from landsat 8 oli/tirs images
    J Mukherjee, J Mukherjee, D Chakravarty, S Aikat
    International Conference on Pattern Recognition and Machine Intelligence 2019

  • Automated seasonal detection of coal surface mine regions from landsat 8 oli images
    J Mukherjee, J Mukhopadhyay, D Chakravarty, S Aikat
    IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium 2019

  • A novel index to detect opencast coal mine areas from landsat 8 OLI/TIRS
    J Mukherjee, J Mukherjee, D Chakravarty, S Aikat
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote 2019

  • Automated seasonal separation of mine and non mine water bodies from landsat 8 OLI/TIRS using clay mineral and iron oxide ratio
    J Mukherjee, J Mukherjee, D Chakravarty
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote 2019

  • Investigation of seasonal separation in mine and non mine water bodies using local feature analysis of landsat 8 OLI/TIRS images
    J Mukherjee, J Mukhopadhyay, D Chakravarty
    IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium 2018

  • Ontology-driven content-based retrieval of heritage images
    D Podder, J Mukherjee, SM Aswatha, J Mukherjee, S Sural
    Heritage Preservation, 143-160 2018

  • Detection of coal seam fires in summer seasons from landsat 8 OLI/TIRS in Dhanbad
    J Mukherjee, J Mukherjee, D Chakravarty
    National Conference on Computer Vision, Pattern Recognition, Image 2017

  • Duplication detection for image sharing systems
    J Mukherjee, SM Aswatha, P Mondal, J Mukherjee, P Mitra
    Proceedings of the 2014 Indian Conference on Computer Vision Graphics and 2014

  • A survey on image retrieval performance of different bag of visual words indexing techniques
    J Mukherjee, J Mukhopadhyay, P Mitra
    Proceedings of the 2014 IEEE Students' Technology Symposium, 99-104 2014

  • VIMARSHAK--A Web Based Subjective Image Evaluation System
    G Garg, P Mondal, SM Aswatha, J Mukherjee, T Maji, J Mukherjee
    2014 Fifth International Conference on Signal and Image Processing, 73-76 2014

  • Real-time retrieval system for heritage images
    S Mishra, J Mukherjee, P Mondal, SM Aswatha, J Mukherjee
    Emerging Research in Electronics, Computer Science and Technology, 245-253 2014

  • Tamoghna Ojha, IIT Kharagpur, India
    J Mukherjee, B Ghosh, PP Pai, AG Roy, A Kakati, A Ghosh, A Mishra, ...


MOST CITED SCHOLAR PUBLICATIONS

  • A survey on image retrieval performance of different bag of visual words indexing techniques
    J Mukherjee, J Mukhopadhyay, P Mitra
    Proceedings of the 2014 IEEE Students' Technology Symposium, 99-104 2014
    Citations: 30

  • A novel index to detect opencast coal mine areas from landsat 8 OLI/TIRS
    J Mukherjee, J Mukherjee, D Chakravarty, S Aikat
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote 2019
    Citations: 15

  • Investigation of seasonal separation in mine and non mine water bodies using local feature analysis of landsat 8 OLI/TIRS images
    J Mukherjee, J Mukhopadhyay, D Chakravarty
    IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium 2018
    Citations: 8

  • Automated seasonal separation of mine and non mine water bodies from landsat 8 OLI/TIRS using clay mineral and iron oxide ratio
    J Mukherjee, J Mukherjee, D Chakravarty
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote 2019
    Citations: 7

  • Automated seasonal detection of coal surface mine regions from landsat 8 oli images
    J Mukherjee, J Mukhopadhyay, D Chakravarty, S Aikat
    IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium 2019
    Citations: 4

  • Ontology-driven content-based retrieval of heritage images
    D Podder, J Mukherjee, SM Aswatha, J Mukherjee, S Sural
    Heritage Preservation, 143-160 2018
    Citations: 4

  • Detection of coal seam fires in summer seasons from landsat 8 OLI/TIRS in Dhanbad
    J Mukherjee, J Mukherjee, D Chakravarty
    National Conference on Computer Vision, Pattern Recognition, Image 2017
    Citations: 4

  • Duplication detection for image sharing systems
    J Mukherjee, SM Aswatha, P Mondal, J Mukherjee, P Mitra
    Proceedings of the 2014 Indian Conference on Computer Vision Graphics and 2014
    Citations: 3

  • VIMARSHAK--A Web Based Subjective Image Evaluation System
    G Garg, P Mondal, SM Aswatha, J Mukherjee, T Maji, J Mukherjee
    2014 Fifth International Conference on Signal and Image Processing, 73-76 2014
    Citations: 3

  • Real-time retrieval system for heritage images
    S Mishra, J Mukherjee, P Mondal, SM Aswatha, J Mukherjee
    Emerging Research in Electronics, Computer Science and Technology, 245-253 2014
    Citations: 3

  • Automated Coastline Detection from Landsat 8 Oli/Tirs Images with the Presence of Inland Water Bodies in Andaman
    R Mondal, J Mukherjee, J Mukhopadhyay
    IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium 2020
    Citations: 2

  • Unsupervised detection of active, new, and closed coal mines with reclamation activity from landsat 8 oli/tirs images
    J Mukherjee, J Mukherjee, D Chakravarty, S Aikat
    International Conference on Pattern Recognition and Machine Intelligence 2019
    Citations: 2

  • Seasonal detection of coal overburden dump regions in unsupervised manner using landsat 8 OLI/TIRS images at jharia coal fields
    J Mukherjee, J Mukherjee, D Chakravarty, S Aikat
    Multimedia Tools and Applications 80 (28), 35605-35627 2021
    Citations: 1