Jit Mukherjee
@bitmesra.ac.in
Assistant Professor, Dept. of CSE
Birla Institute of Technology
RESEARCH INTERESTS
Remote Sensing, Image Processing, Computer Vision, Machine Learning, Multimedia System
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Jit Mukherjee
IEEE
Coal mining suffers heavily from illegal mines in different paradigms of social, economical, and environmental welfare of a country. It is complex to separate legal and illegal mines using spectral characteristics exclusively. As coal dusts spreads along the surroundings of coal transportation, detection of roads, which are used for transporting raw coals from the other roads through spectral properties can be found instrumental for locating such illegal mines. In this paper, a novel technique has been proposed to detect such roads by analyzing their spectral properties without any labelled dataset. First, road extraction index (REI) is studied to detect probable roads. As coal mine index (CMI) preserves high mineral content regions, CMI values of these detected roads are examined. With varying threshold values of CMI the proposed technique can separate the roads inside a mining region, roads leaving a mining region, and roads not being used for transporting coals.
Jit Mukherjee
IEEE
Though different water indexes can detect rivers with significant width, detection of a narrow river trail is a complex process as various other land classes have near similar water index values. In the past, various techniques on detection of river networks consider rivers with significant width and as a continuous trail primarily using digital terrain (DTM) and digital elevation (DEM) models. Further, road networks are also found to have near similar spectral characteristics with narrow rivers. Moreover, a narrow river shows high sinuosity than other river networks. The paper presents a novel technique to detect rivers with varying width without labelled dataset. Firstly, water index values are enhanced by gabor filters and adaptive histogram equalization. Next, rivers with significant width and narrow rivers are detected separately using connected component and bounding box analysis.
Prateek Tewary and Jit Mukherjee
Springer Nature Switzerland
Jit Mukherjee, Jayanta Mukhopadhyay, and Debashish Chakravarty
IEEE
Though surface mining causes huge land use and land cover changes, it is a widely used technique. Many ores like Iron, Coal, Copper, Dolomite, Diamond, Gold, etc. are extracted using surface mining. In literature, semi supervised and supervised method are used for classification and monitoring of surface mining regions. Spectral indexes to detect these surface mine regions are yet to be defined. An index namely, coal mine index (CMI), has been proposed to detect surface coal mine regions. The paper focuses on performance of CMI in seven different surface mining regions. In this regard, a brief survey on performance and applicability of CMI over these surface mining regions are discussed in this paper.
Jit Mukherjee
IEEE
Coal seam fire (CSF) affects various social, economical, and environmental aspects. Surface and sub-surface CSF are detected in the past by studying the anomalies of land surface temperature patterns. Most of them use manual observations and thresholds to separate CSF. Such techniques are also susceptible to other regions, which have high temperature and high mineral content. The proposed work presents a novel technique to detect surface and sub-surface CSF by treating them as outliers. It provides a multi-level anomaly detection technique using isolation forest, which is robust to the presence of other high temperature and high mineral content regions over the seasons.
Jit Mukherjee, Jayanta Mukherjee, Debashish Chakravarty, and Subhash Aikat
Springer Science and Business Media LLC
Jit Mukherjee, Jayanta Mukhopadhyay, Debashish Chakravarty, and Subhas Aikat
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.
Rajdeep Mondal, Jit Mukherjee, and Jayanta Mukhopadhyay
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.
Jit Mukherjee, Jayanta Mukherjee, and Debashish Chakravarty
Springer Singapore
Jit Mukherjee, Jayanta Mukhopadhyay, Debashish Chakravarty, and Subhas Aikat
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.
Jit Mukherjee, Jayanta Mukherjee, and Debashish Chakravarty
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.
Jit Mukherjee, Jayanta Mukherjee, Debashish Chakravarty, and Subhas Aikat
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.
Jit Mukherjee, Jayanta Mukherjee, Debashish Chakravarty, and Subhas Aikat
Springer International Publishing
Jit Mukherjee, Jayanta Mukhopadhyay, and Debashish Chakravarty
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.
Dipannita Podder, Jit Mukherjee, Shashaank Mattur Aswatha, Jayanta Mukherjee, and Shamik Sural
Springer Singapore
Jit Mukherjee, Jayanta Mukherjee, and Debashish Chakravarty
Springer Singapore
Jit Mukherjee, Shashaank M. Aswatha, Prasenjit Mondal, Jayanta Mukherjee, and Pabitra Mitra
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.
Jit Mukherjee, Jayanta Mukhopadhyay, and Pabitra Mitra
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.
Gazal Garg, Prasenjit Mondal, Shashaank M. Aswatha, Jit Mukherjee, Tapas Maji, and Jayanta Mukherjee
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.
Sumit Mishra, Jit Mukherjee, Prasenjit Mondal, Shashaank M. Aswatha, and Jayanta Mukherjee
Springer India